eLife Assessment

This important work examines how microexons contribute to brain activity, structure, and behavior. The authors find that loss of microexon sequences generally has subtle impacts on these metrics in larval zebrafish, with few exceptions. The evidence is solid, using modern high-throughput phenotyping methodology in zebrafish. Overall, this work will be of interest to neuroscientists and generate further studies of interest to the field.

Reviewer #1 (Public review):

Summary:

The authors use high-throughput gene editing technology in larval zebrafish to address whether microexons play important roles in the development and functional output of larval circuits. They find that individual microexon deletions rarely impact behavior, brain morphology, or activity, and raise the possibility that behavioral dysregulation occurs only with more global loss of microexon splicing regulation. Other possibilities exist: perhaps microexon splicing is more critical for later stages of brain development, perhaps microexon splicing is more critical in mammals, or perhaps the behavioral phenotypes observed when microexon splicing is lost are associated with loss of splicing in only a few genes.

Strengths:

- The authors provide a qualitative analysis of microexon inclusion during early zebrafish development

- The authors provide comprehensive phenotyping of microexon mutants, addressing the role of individual microexons in the regulation of brain morphology, activity, and behavior.

Reviewer #3 (Public review):

Summary:

This paper sought to understand how microexons influence early brain function. By selectively deleting a large number of conserved microexons and then phenotyping the mutants with a behavior and brain activity assays, the authors find that most microexons have minimal effects on the global brain activity and broad behaviors of the larval fish-- although a few do have phenotypes.

Strengths:

The work takes full advantage of the scale that is afforded in zebrafish, generating a large mutant collection that is missing microexons and systematically phenotyping them with high throughput behaviour and brain activity assays. The work lays an important foundation for future studies that seek to uncover the likely subtle roles that single microexons will play in shaping development and behavior.

Weaknesses:

Although the manuscript includes evidence for many mutants that microexon deletion has minimal effect on full length transcript levels, some of the microexon loss does alter transcript levels. Since the mutations usually yielded no phenotype, these effects on full-length transcripts are unlikely to be a major confound. For mircoexon mutants displaying phenotypes, future work will have to tease apart whether secondary effects on the transcripts are contributing to the phenotype.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

The authors use high-throughput gene editing technology in larval zebrafish to address whether microexons play important roles in the development and functional output of larval circuits. They find that individual microexon deletions rarely impact behavior, brain morphology, or activity, and raise the possibility that behavioral dysregulation occurs only with more global loss of microexon splicing regulation. Other possibilities exist: perhaps microexon splicing is more critical for later stages of brain development, perhaps microexon splicing is more critical in mammals, or perhaps the behavioral phenotypes observed when microexon splicing is lost are associated with loss of splicing in only a few genes.

A few questions remain:

(1) What is the behavioral consequence for loss of srrm4 and/or loss-of-function mutations in other genes encoding microexon splicing machinery in zebrafish?

It has been established that srrm4 mutants exhibit no overt morphological phenotypes and are not visually impaired (Ciampi et al., 2022). We are coordinating our publication with Lopez-Blanch et al. (https://doi.org/10.1101/2024.10.23.619860), which shows that srrm4 mutants also have minimal behavioral phenotypes. In contrast, srrm3 mutants have severe vision loss, early mortality, and numerous neural and behavioral phenotypes (Ciampi et al., 2022; Lopez-Blanch et al., 2024). We now point out the phenotypes of srrm3/srrm4 mutants in the manuscript.

We chose not to generate and characterize the behavior and brain activity of srrm3/srrm4 mutants for two reasons: 1) we were aware of two other labs in the zebrafish community that had generated srrm3 and/or srrm4 mutants (Ciampi et al., 2022 and Gupta et al., 2024, https://doi.org/10.1101/2024.11.29.626094; Lopez-Blanch et al., 2024, https://doi.org/10.1101/2024.10.23.619860), and 2) we were far more interested in determining the importance of individual microexons to protein function, rather than loss of the entire splicing program. Microexon inclusion can be controlled by different splicing regulators, such as srrm3 (Ciampi et al., 2022) and possibly other unknown factors. Genetic compensation in srrm4 mutants could also result in microexons still being included through actions of other splicing regulators, complicating the analysis of these regulators. We mention srrm4 in the manuscript to point out that some selected microexons are adjacent to regulatory elements expected of this pathway. We did not, however, choose microexons to mutate based on whether they were regulated by Srrm4, making the characterization of srrm3/srrm4 mutants disconnected from our overarching project goal.

We have edited the Introduction as follows to clarify our goal: “Studies of splicing regulators such as srrm4 impact the entire splicing program, making it impossible to determine the importance of individual microexons to protein function. Further, microexons could still be differentially included in a regulatory mutant via compensation by other splicing factors ...”

(2) What is the consequence of loss-of-function in microexon splicing genes on splicing of the genes studied (especially those for which phenotypes were observed).

We are unclear whether “microexon splicing genes” refers to the splicing regulators srrm3/srrm4, which we choose not to study in this work (see response to point #1 above), or the genes that contain microexons. The severe visual phenotypes of srrm3 mutants confounds the study of microexon splicing in this line because altered splicing levels could be due to downstream changes in this significantly different developmental context. A detailed discussion of splicing consequences on removal of microexons from microexoncontaining genes is in the response to point #4 below.

(3) For the microexons whose loss is associated with substantial behavioral, morphological, or activity changes, are the same changes observed in loss-of-function mutants for these genes?

In the first version of the manuscript, we had included two explicit comparisons of microexon loss with a standard loss-of-function allele, one with a phenotype and one without, in Figure S1 (now Figures S3 and S4) of this manuscript. Beyond the two pairs we had included, Lopez-Blanch et al. (https://doi.org/10.1101/2024.10.23.619860) described mild behavioral phenotypes for a microexon removal for kif1b, and we showed developmental abnormalities for the kif1b loss-of-function allele (now Figure S3). We have now added a predicted protein-truncating allele for ppp6r3. This new line has phenotypes that are similar but slightly stronger in brain activity and structure than the mutant that lacks only the microexon. The prior Figure S1 (now Figures S3 and S4) was only briefly mentioned in the first version of the manuscript, and we now clarify this point in the Results: “Protein-truncating mutations in eleven additional genes that contain microexons revealed developmental and neural phenotypes in zebrafish (Figure S3, Figure S4), indicating that the genes themselves are involved in biologically relevant pathways. Three of these genes– tenm4, sptan1, and ppp6r3 – are also in our microexon line collection.”

Additionally, we can draw expected conclusions from the literature, as some genes with our microexon mutations have been studied as typical mutants in zebrafish or mice. We have modified our manuscript to include a discussion of both loss-of-function zebrafish and mouse mutants. See the response to below point #4.

(4) Do "microexon mutations" presented here result in the precise loss of those microexons from the mRNA sequence? E.g. are there other impacts on mRNA sequence or abundance?

We acknowledge that unexpected changes to the mRNA of the tested mutants could occur following microexon removal. In particular, all regulatory elements should be removed from the region surrounding the microexon, as any remaining elements could drive the inclusion of unexpected exons that result in premature stop codons.

First, we have clarified our generated mutant alleles by adding a figure (Figure S1) that details the location of the gRNA cut sites in relation to the microexon, its predicted regulatory elements, and its neighboring exons.

Second, we have experimentally determined whether the mRNA was modified as expected for a subset of mutants with phenotypes. In all eight tested lines (Figure S2), the microexon was precisely eliminated without causing any other effects on the sequence of the transcript in the neighboring region. We did, however, observe an effect on transcript abundance for one homozygous mutant (vav2). It is possible that complex forms of genetic regulation are occurring that are not induced by unexpected isoforms or premature stop codons. Interestingly, Lopez-Blanch et al. (https://doi.org/10.1101/2024.10.23.619860) eliminated a different microexon in vav2 and also observed a subtle well center preference. If their allele from an entirely different intronic region also results in transcript downregulation, it would support the hypothesis of genetic compensation through atypical pathways. If not, it is likely this phenotype is due specifically to removal of the microexon protein sequence. Not all mutants with phenotypes could be assessed with qRT-PCR because some were no longer present in the lab. All lines were generated in a similar way, however, removing both the microexon and neighboring regulatory elements while avoiding the neighboring exons. Accordingly, we now also explicitly point out those where the clean loss of the microexon was confirmed (eif4g3b, ppp6r3, sptan1, vti1a, meaf6, nrxn1a, tenm3) and those with possibly interesting phenotypes that were not confirmed (ptprd-1, ptprd-2, rapgef2, dctn4, dop1a, mapk8ip3).

Third, we have further emphasized in the manuscript that these observed phenotypes are extremely mild compared to those observed in over one hundred protein-truncating mutations we have assessed in previous (Thyme et al., 2019; Capps et al., 2024) and unpublished ongoing work. We showed data for one mutant, tcf7l2, which we consider to have moderately strong neural phenotypes, and we have extended this comparison in the revision (new Figure 3G). Additionally, loss-of-function alleles for some microexoncontaining genes have strong developmental phenotypes, as we showed in Figure S1 (now Figures S3 and S4) of this manuscript in addition to our published work (Thyme et al., 2019; Capps et al., 2024). It is known from the literature that the loss-of-function mutants for mapk8ip3 are stronger than we observed here (Tuttle., et al., 2019), suggesting that only the microexon is removed in our line. The microexons in Ptprd are also well-studied in mice, and we expect that only the microexon was removed in our lines. Both Dctn4 and Rapgef2 are completely lethal prior to weaning in mice (the International Mouse Phenotyping Consortium).

(5) Microexons with a "canonical layout" (containing TGC / UC repeats) were selected based on the likelihood that they are regulated by srrm4. Are there other parallel pathways important for regulating the inclusion of microexons? Is it possible to speculate on whether they might be more important in zebrafish or in the case of early brain development?

The microexons were not selected based on the likelihood that they were regulated by Srrm4. We have clarified the manuscript regarding this point. There are parallel pathways that can control the inclusion of microexons, such as Srrm3 (Ciampi et al., 2022). It is wellknown that loss of srrm3 has a stronger impact on zebrafish development than srrm4 (Ciampi et al., 2022). The goal of our work was not to investigate these splicing regulators but instead to determine the individual importance of these highly conserved protein changes.

Strengths:

(1) The authors provide a qualitative analysis of splicing plasticity for microexons during early zebrafish development.

(2) The authors provide comprehensive phenotyping of microexon mutants, addressing the role of individual microexons in the regulation of brain morphology, activity, and behavior.

We thank the reviewer for their support. The pErk brain activity mapping method is highly sensitive, significantly minimizing the likelihood that the field has simply not looked hard enough for a neural phenotype in these microexon mutants. In our published work (Thyme et al., 2019), we show that brain activity can be drastically impacted without manifesting in differences in those behaviors assessed in a typical larval screen (e.g., tcf4, cnnm2, and more).

Weaknesses:

(1) It is difficult to interpret the largely negative findings reported in this paper without knowing how the loss of srrm4 affects brain activity, morphology, and behavior in zebrafish.

See response to point 1.

(2) The authors do not present experiments directly testing the effects of their mutations on RNA splicing/abundance.

See response to point 4.

(3) A comparison between loss-of-function phenotypes and loss-of-microexon splicing phenotypes could help interpret the findings from positive hits.

See response to points 3 and 4.

Reviewer #2 (Public review):

Summary:

The manuscript from Calhoun et al. uses a well-established screening protocol to investigate the functions of microexons in zebrafish neurodevelopment. Microexons have gained prominence recently due to their enriched expression in neural tissues and misregulation in autism spectrum disease. However, screening of microexon functionality has thus far been limited in scope. The authors address this lack of knowledge by establishing zebrafish microexon CRISPR deletion lines for 45 microexons chosen in genes likely to play a role in CNS development. Using their high throughput protocol to test larval behaviour, brain activity, and brain structure, a modest group of 9 deletion lines was revealed to have neurodevelopmental functions, including 2 previously known to be functionally important.

Strengths:

(1) This work advances the state of knowledge in the microexon field and represents a starting point for future detailed investigations of the function of 7 microexons.

(2) The phenotypic analysis using high-throughput approaches is sound and provides invaluable data.

We thank the reviewer for their support.

Weaknesses:

(1) There is not enough information on the exact nature of the deletion for each microexon.

To clarify the nature of our mutant alleles, we have added a figure (Figure S1) that details the location of the microexon in relation to its predicted neighboring exons, deletion boundaries, guide RNAs, and putative regulatory elements.

(2) Only one deletion is phenotypically analysed, leaving space for the phenotype observed to be due to sequence modifications independent of the microexon itself.

We have determined whether the mRNA is impacted in unanticipated ways for a subset of mutants with mild phenotypes (see point #4 responses to Reviewer 1 for details). Our findings for three microexon mutants (ap1g1, vav2, and vti1a) are corroborated by LopezBlanch et al. (https://doi.org/10.1101/2024.10.23.619860). We have also already compared the microexon removal to a loss-of-function mutant for two lines (Figures S3 and S4), and we have made this comparison more obvious as well as increasing the discussion of the expected phenotypes from typical loss-of-function mutants (see point #3 response to reviewer 1).

Unlike protein-coding truncations, clean removal of the microexon and its regulatory elements is unlikely to yield different phenotypic outcomes if independent lines are generated (with the exception of genetic background effects). When generating a proteintruncating allele, the premature stop codon can have different locations and a varied impact on genetic compensation. In previous work (Capps et al., 2024), we have observed different amounts of nonsense-mediated decay-induced genetic compensation (El-Brolosy, et al., 2019) depending on the location of the mutation. As they lack variable premature stop codons (the expectation of a clean removal), two mutants for the same microexons should have equivalent impacts on the mRNA.

We now address the concern of subtle genetic background effects in the Methods: “Even with using sibling controls and collecting multiple biological replicates from individual parents, the possibility remains that linked genetic variation may have contributed to the mild phenotypes we observed, as only a single line was generated.”

Reviewer #3 (Public review):

Summary:

This paper sought to understand how microexons influence early brain function. By selectively deleting a large number of conserved microexons and then phenotyping the mutants with behavior and brain activity assays, the authors find that most microexons have minimal effects on the global brain activity and broad behaviors of the larval fish-- although a few do have phenotypes.

Strengths:

The work takes full advantage of the scale that is afforded in zebrafish, generating a large mutant collection that is missing microexons and systematically phenotyping them with high throughput behaviour and brain activity assays. The work lays an important foundation for future studies that seek to uncover the likely subtle roles that single microexons will play in shaping development and behavior.

We thank the reviewer for their support.

Weaknesses:

The work does not make it clear enough what deleting the microexon means, i.e. is it a clean removal of the microexon only, or are large pieces of the intron being removed as well-- and if so how much? Similarly, for the microexon deletions that do yield phenotypes, it will be important to demonstrate that the full-length transcript levels are unaffected by the deletion. For example, deleting the microexon might have unexpected effects on splicing or expression levels of the rest of the transcript that are the actual cause of some of these phenotypes.

To clarify the nature of our mutant alleles, we have added a figure (Figure S1) that details the location of the microexon in relation to its predicted neighboring exons, deletion boundaries, guide RNAs, and putative regulatory elements. We have determined whether the mRNA is impacted in unanticipated ways for a subset of mutants with mild phenotypes (see point #4 responses to Reviewer 1 for details).

Reviewer #1 (Recommendations for the authors):

(1) For most ME mutations, 4 guide sequences are provided. More description / a diagram could be helpful to interpret how ME mutations were generated.

We have added diagrams to the Supplementary Materials (new Figure S1) to show where the guide RNAs, cut sites, and putative regulatory elements are in relationship to the microexon and its neighboring exons. We have also added the following point to the text: “Four guide RNAs were used, two on each side of the microexon (Table S2, Figure S1).”

(2) Figure 1 indicates that there are 45 microexons (MEs) but the text initially indicates that there are 44 that exist in a canonical layout (the text later indicates there are 45). This could be made more clear.

The 45 refers to the mutants that were generated, not the microexons with putative Srrm4 regulatory elements. We did not choose microexons to mutate based on whether they were regulated by Srrm4. We have clarified these points in the manuscript as follows: “Of these 95 microexons, 42 exist in a canonical layout in the zebrafish genome, with both a UGC and UC repeat – or similar polypyrimidine tract – directly upstream of the alternatively spliced exon (Gonatopoulos-Pournatzis et al., 2018) (Table S1), indicating that Srrm4 likely controls their inclusion. Of the remaining microexons, 44 are organized similarly to the canonical layout, typically with either a UGC or UC repeat. Thus, they may also be regulated by Srrm4.” and “Using CRISPR/Cas9, we generated lines that removed 45 conserved microexons  (Table S2) and assayed larval brain activity, brain structure, and behavior (Figure 1A). Four guide RNAs were used, two on each side of the microexon (Table S2, Figure S1). For microexons with upstream regulatory elements that are likely important for splicing, these elements were also removed (Figure S1).”

(3) The description of the "canonical layout" as containing TGC / UC repeats could be rewritten as either "containing a UGC motif and UC repeats" or "containing a TGC motif and TC repeats."

This error has been corrected.

(4) Why was tcf7l2 selected as a control for MAP mapping?

The mutant for tcf7l2 is an example of a moderately strong phenotype from a recent study we completed (Capps et al., 2025). This mutant was selected because it has both increased and decreased activity and structure and is ideal for setting the range of the graph. We now include a comparison to additional mutants from this study of autism genes (Capps et al., 2025) to further demonstrate how mild the phenotypes are in the microexon removal mutants (new Figure 3G). We also include the activity and structure maps of tcf7l2 mutants in Supplementary Figures 9 and 11.

(5) What does it mean that of the remaining microexons, most are similar to canonical layout?

Typically, they would have one of the two regulatory elements instead of both or the location of the possible elements would be slightly farther away than expected. We have clarified this point in the manuscript as follows: “Of these 95 microexons, 42 exist in a canonical layout in the zebrafish genome, with both a UGC and UC repeat  or similar polypyrimidine tract – directly upstream of the alternatively spliced exon (Gonatopoulos-Pournatzis et al., 2018) (Table S1), indicating that Srrm4 likely controls their inclusion. Of the remaining microexons, 44 are organized similarly to the canonical layout, typically with either a UGC or UC repeat. Thus, they may also be regulated by Srrm4.”

(6) Figure 2A is very difficult to see - most are either up or down - suggest splitting into 2 figures - one = heat map, second can summarize values that were both up and down.

We prefer to retain this information for accuracy. The bubble location is offset to effectively share the box between the orange (decreased) and purple (increased) measures. For example, and as noted in the methods and now expanded upon, a measure can change between 4 and 6 dpf or a measure such as bout velocity could be increased while the distance traveled is decreased (both are magnitude measures). The offset of the bubbles is consistently 0.2 data units in x and y from the center of the box.

(7) The authors apply rigorous approaches to testing the importance of microexons. I especially appreciate the inclusion of separate biological replicates in the main figures!

We thank the reviewer for their positive feedback.

(8) Page 5 line 5 - suggest "compared to homozygous mutants".

The change has been made.

(9) For Eif5g3b dark flash phenotype, it's not clear what "p-values are not calculated for response plots" means. A p-value is provided in the plot for ppp6r3 response freq.

The eif4g3b plot is the actual response trace measuring through pixel changes whereas the ppp6r3 is the frequency of response. While informative, the response plot is time-based data with a wide dynamic range, making the average signal across the entire time window meaningless. We include the p-values for a related measure, the latency for the first 10 dark flashes in block 1 (day6dpfdf1a_responselatency) in the legend.

(10) The ptprd phenotype in 2D is not described in the text.

The change has been made.

(11) Page 7 line 7: "mild" is repeated.

This error has been corrected.

Reviewer #2 (Recommendations for the authors):

Specific points for needed improvement:

(1) The title should be adjusted to more accurately describe the results. The term 'minimal' is under-representing the findings. 9/45 (20%) of targets in their screen have some phenotype, indicating that a significant number have indeed an important function. Moreover, the phenotypic analysis is limited, leaving space for missed abnormalities (as discussed by the authors). I would therefore suggest a more neutral title such as 'Systematic genetic deletion of microexons uncovers their roles in zebrafish brain development and larval behaviour'.

While some microexon mutants do have repeatable phenotypes, these phenotypes are far milder than phenotypes observed in other mutant sets. We now include a comparison to additional mutants from this study of autism genes (Capps et al.,2025) to further demonstrate how mild the phenotypes are in the microexon removal mutants (new Figure 3G). The title states that these microexons have a minimal impact on larval zebrafish brain morphology and function, leaving room for the possibility of adult phenotypes. Thus, we prefer to retain this title.

(2) Do the 45 chosen microexons correspond to the 44 with a canonical layout with TGC and UC repeats? If so, it needs to be explicitly stated in the text that exons were chosen for mutation based on the potential for SRRM4 regulation. If not, then the rationale for the choice of the 45 mutants from the 95 highly conserved events needs to be explained further.

The 45 refers to the mutants that were generated, not the microexons with putative Srrm4 regulatory elements. We did not choose microexons to mutate based on whether they were regulated by Srrm4. We have clarified these points in the manuscript as follows: “Of these 95 microexons, 42 exist in a canonical layout in the zebrafish genome, with both a UGC and UC repeat – or similar polypyrimidine tract – directly upstream of the alternatively spliced exon (Gonatopoulos-Pournatzis et al., 2018) (Table S1), indicating that Srrm4 likely controls their inclusion. Of the remaining microexons, 44 are organized similarly to the canonical layout, typically with either a UGC or UC repeat. Thus, they may also be regulated by Srrm4.” and “Using CRISPR/Cas9, we generated lines that removed 45 conserved microexons (Table S2) and assayed larval brain activity, brain structure, and behavior (Figure   1A). Four guide RNAs were used, two on each side of the microexon (Table S2, Figure S1). For microexons with upstream regulatory elements that are likely important for splicing, these elements were also removed (Figure S1).”

There was no clear rationale for those that were selected. We attempted to generate all 95 and some mutants were not successfully generated in our initial attempt. As we found minimal phenotypes, we elected to not continue to make the remaining ones on the list.

(3) More detail regarding the design of guides for CRISPR is required in the text in the methods section. From Table S2, 4 guides were used per microexon. Were these designed to flank the microexon? How far into the intronic sequence were the guides designed? Were the splicing regulatory sequences (polypyrimidine tract, branchpoint) also removed? The flanking sequences of each of the 45 deletion lines need to be provided.

We have added diagrams to the Supplementary Materials (new Figure S1) to show where the guide RNAs, cut sites, and putative regulatory elements are in relationship to the microexon and its neighboring exons. We removed the microexon and the surrounding area that contains the putative regulatory elements.

(4) Following on from the previous point, to ascertain that the phenotype observed is truly due to lack of microexon (rather than other event linked to removed intronic sequences) - for the 7 exons newly identified as functionally important, at least one added deletion line has to be shown, presenting the same phenotype. If making 7 more lines can't be achieved in a reasonable time (we are aware this is a big ask), a MO experiment blocking microexon splicing needs to be provided (may not be ideal for analysis at 6 dpf). For the existing mutants and the new ones (or morphants), sequencing of the mRNAs for the 7 genes in mutants and siblings also needs to be added to check any possible change in other variants.

Unlike protein-coding truncations, clean removal of the microexon and its regulatory elements is unlikely to yield different phenotypic outcomes if independent lines are generated (with the exception of genetic background effects). When generating a protein-truncating allele, the premature stop codon can have different locations and a varied impact on genetic compensation. In previous work (Capps et al., 2024), we have observed different amounts of nonsense-mediated decay-induced genetic compensation (El-Brolosy, et al., 2019) depending on the location of the mutation. As they lack variable premature stop codons (the expectation of a clean removal), two mutants for the same microexons should have equivalent impacts on the mRNA. We acknowledge that we inadequately described the generation of these alleles, and we now provide Figure S1 to show the microexon’s relationship to possible regulatory elements that impact splicing in unexpected ways if they remain.

We now acknowledge the concern of subtle genetic background effects in the Methods: “Even with using sibling controls and collecting multiple biological replicates from individual parents, the possibility remains that linked genetic variation may have contributed to the mild phenotypes we observed, as only a single line was generated.”

Given the caveats of MOs and transient microinjection for the study of 6 dpf phenotypes, we disagree that this suggested experiment would provide value. The phenotypic assays we use are highly sensitive, and we would not even trust CRISPANTs to yield reliable data. We have added an additional loss-of-function allele for ppp6r3 from the Sanger knockout project, which has a similar but stronger size change to the ppp6r3 microexon-removal line. In addition, our findings for three microexon mutants (ap1g1, vav2, and vti1a) are corroborated by Lopez-Blanch et al. (https://doi.org/10.1101/2024.10.23.619860).

To support that these we generated clean removal of these microexons, we experimentally determined whether the mRNA is impacted in unanticipated ways for a subset of mutants with mild phenotypes (see the point #4 public response to Reviewer 1). We also have already compared the microexon removal to a loss-offunction mutant for two lines (Figure S1), and we have made that outcome more obvious as well as increasing the discussion of the expected phenotypes from typical loss-of-function mutants (see point #3 public response to Reviewer 1).

(5) Figure 3: An image of control tcf7l2 mutant brain activity as a reference should be included.

We now include the activity and structure maps of tcf7l2 mutants in Supplementary Figures 9 and 11.

(6) Figure 3a/b. The gene names on the y-axis of the pERK and structure comparisons should be reordered to be alphabetical so that phenotypes can be compared by the reader for the same microexon across the two assays.

These data are clustered so that any similarities between maps can be recognized. We prefer to retain the clustering to compare lines to each other.

(7) Figure S6 legend. Including graph titles like "day3msdf_dpix_numberofbouts_60" is not comprehensible to the reader so should be replaced with more descriptive text. As should jargon such as "combo plot" and"habituation_day5dpfhab1post_responsefrequency_1_a1f1000d5p" etc.

The legend has been edited to describe the experiments. Subsections of the prior names are maintained in parentheses to enable the reader to connect the plots in this figure to the specific image and underlying data in Zenodo.

(8) Page 2 line 21 "to enable proper".

The change has been made.

(9) Page 7 line 7. Repeatable phenotypes were mild mild.

This error has been corrected.

Reviewer #3 (Recommendations for the authors):

(1) Figure 1B is confusingly laid out.

We are unclear how to modify Figure 1B, as it is a bar plot. We have modified several figures to improve clarity.

(2) Figure 1E-there are some pictures of zebrafish but to what end? They aren't labelled. The dark "no expression" looks really similar to the dark green, "high expression".

The zebrafish images represent the ages assessed for microexon inclusion. We have added labels to clarify this point.

(3) The main text says "microexons were removed by Crispr" but there is no detail in the main text about this at all-- and barely any in the methods. What does it mean to be removed? Cleanly? Or including part of the introns on either side? Etc. How selected, raised, etc? I can glean some of this from the Table S2 if I do a lot of extra work, but at least some notes about this would be important.

We have added diagrams to the Supplementary Materials (new Figure S1) to show where the guide RNAs, cut sites, and putative regulatory elements are in relationship to the microexon and its neighboring exons. We removed the microexon and the surrounding area that contains the putative regulatory elements.

(4) Figure 2 - There are no Ns, at least for the plots on the right. The reader shouldn't have to dig deep in Table S2 to find that. It is also unclear why heterozygous fish are not included in these analyses, since there are sibling data for all. Removed for readability of the plots might be warranted, but this should be made explicitly clear.

The Ns for these plots have been added to the legend. The legend was also modified as follows: “Comparisons to the heterozygous larvae are removed for clarity and available in the Supplementary Materials, as they often have even milder phenotypes than homozygous.”

(5) Needed data: for those with phenotypes, some evidence should be presented that the full-length transcripts that encode proteins without the microexons are still expressed at the same level and without splicing errors/NMD. Otherwise, some of these phenotypes that were found could be due to knockdown or LOF (or I suppose even overexpression) of the targeted gene.

We have added a new Supplementary Figure S2 confirming clean removal of the microexons with RT-PCR for a subset of mutants with phenotypes. This figure also includes qRT-PCR for the same subset. We now discuss these findings: Results: “For eight mutant lines, we confirmed that the microexon was eliminated from the transcripts as expected (Figure S2). Although our genomic deletion did not yield unexpected isoforms, qRT-PCR on these eight lines revealed significant downregulation for the homozygous vav2 mutant (Figure S2), indicating possibly complex genetic regulation.”

These "capacities" are a blueprint for how HE can notice, name, and credential the stuff that actually matters.

So Tenmu became emperor and immediately used religious practices to get his supporters to the top of the chain?

This makes me think of creation and God as the author of creation. The language is really beautiful and makes the reader think. It kind of points to the idea and mystery of where did God come from. "Breathed by its own nature, apart from nothing whatsoever". It also points to the idea of one god, or a monotheistic theme.

Is this questioning whether creation was intentional or not? Or is it questioning if He actually created things?

I found this super interesting, as it implies that neither death nor immortality existed, as there was nothing to differentiate the two.

eLife Assessment

This fundamental study explores a novel cellular mechanism underlying the degeneration of locus coeruleus neurons during chronic restraint stress. The evidence supporting the overexcitation of LC neurons after chronic stress is compelling. The topic is timely, the proposed mechanistic pathway is innovative, and the findings have translational relevance, particularly regarding therapeutic strategies targeting α2A-AR internalization in neurodegenerative diseases.

Reviewer #1 (Public review):

This study aims to elucidate the mechanisms by which stress-induced α2A-adrenergic receptor (α2A-AR) internalization leads to cytosolic noradrenaline (NA) accumulation and subsequent neuronal dysfunction in the locus coeruleus (LC). While the manuscript presents an interesting but ambitious model involving calcium dynamics, GIRK channel rundown, and autocrine NA signaling, several key limitations undermine the strength of the conclusions.

First, the revision does not include new experiments requested by reviewers to validate core aspects of the mechanism. Specifically, there is no direct measurement of cytosolic NA levels or MAO-A enzymatic activity to support the link between receptor internalization and neurochemical changes. The authors argue that such measurements are either not feasible or beyond the scope of the study, leaving a significant gap in the mechanistic chain of evidence.

Second, the behavioral analysis remains insufficient to support claims of cognitive impairment. The use of a single working memory test following an anxiety test is inadequate to verify memory dysfunction behaviors. Additional cognitive assays, such as the Morris Water Maze or Novel Object Recognition, are recommended but not performed.

Third, concerns regarding the lack of rigor in differential MAO-A expression in fluorescence imaging were not addressed experimentally. Instead of clarifying the issue, the authors moved the figure to supplementary data without providing further evidence (e.g., an enzymatic assay or quantitative reanalysis of Western blot, or re-staining of IF for MAO-A) to support their interpretation.

Fourth, concerns regarding TH staining remain unresolved. In Figure S7, the α2A-AR signal appears to resemble TH staining, and vice versa, raising the possibility of labeling errors. It is recommended that the authors re-examine this issue by either double-checking the raw data or repeating the immunostaining to validate the staining.

Overall, the manuscript offers a potentially interesting framework but falls short in providing the experimental rigor necessary to establish causality. The reliance on indirect reasoning and reorganizing existing data, rather than generating new evidence, limits the overall impact and interpretability of the study.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the mechanism by which chronic stress induces degeneration of locus coeruleus (LC) neurons. The authors demonstrate that chronic stress leads to the internalization of α2A-adrenergic receptors (α2A-ARs) on LC neurons, causing increased cytosolic noradrenaline (NA) accumulation and subsequent production of the neurotoxic metabolite DOPEGAL via monoamine oxidase A (MAO-A). The study suggests a mechanistic link between stress-induced α2A-AR internalization, disrupted autoinhibition, elevated NA metabolism, activation of asparagine endopeptidase (AEP), and Tau pathology relevant to Alzheimer's disease (AD). The conclusions of this paper are well-supported mainly by the data, but some aspects of image acquisition require further examination.

Strengths:

This study clearly demonstrates the effects of chronic stimulation on the excitability of LC neurons using electrophysiological techniques. It also elucidates the role of α2-adrenergic receptor (α2-AR) internalization and the associated upstream and downstream signaling pathways of GIRK-1, using a range of pharmacological agents, highlighting the innovative nature of the work. Additionally, the study identifies the involvement of the MAO-A-DOPEGAL-AEP pathway in this process. The topic is timely, the proposed mechanistic pathway is compelling, and the findings have translational relevance, particularly in relation to therapeutic strategies targeting α2A-AR internalization in neurodegenerative diseases.

Weaknesses:

(1) The manuscript reports that chronic stress for 5 days increases MAO-A levels in LC neurons, leading to the production of DOPEGAL, activation of AEP, and subsequent tau cleavage into the tau N368 fragment, ultimately contributing to neuronal damage. However, the authors used wild-type C57BL/6 mice, and previous literature has indicated that AEP-mediated tau cleavage in wild-type mice is minimal and generally insufficient to cause significant behavioral alterations. Please clarify and discuss this apparent discrepancy.

(2) It is recommended that the authors include additional experiments to examine the effects of different durations and intensities of stress on MAO-A expression and AEP activity. This would strengthen the understanding of stress-induced biochemical changes and their thresholds.

(3) Please clarify the rationale for the inconsistent stress durations used across Figures 3, 4, and 5. In some cases, a 3-day stress protocol is used, while in others, a 5-day protocol is applied. This discrepancy should be addressed to ensure clarity and experimental consistency.

(4) The abbreviation "vMAT2" is incorrectly formatted. It should be "VMAT2," and the full name (vesicular monoamine transporter 2) should be provided at first mention.

Comments on revisions:

The authors have addressed all of the reviewers' comments.

Reviewer #3 (Public review):

Summary:

The authors present a technically impressive dataset showing that repeated excitation or restraint stress internalises somatodendritic α2A adrenergic autoreceptors (α2A ARs) in locus coeruleus (LC) neurons. Loss of these receptors weakens GIRK-dependent autoinhibition, raises neuronal excitability, and is accompanied by higher MAO A, DOPEGAL, AEP, and tau N368 levels. The work combines rigorous whole-cell electrophysiology with barbadin-based trafficking assays, qPCR, Western blotting, and immunohistochemistry. The final schematic is appealing and, in principle, could explain early LC hyperactivity followed by degeneration in ageing and Alzheimer's disease.

Strengths:

- Multi-level approach - The study integrates electrophysiology, pharmacology, mRNA quantification, and protein-level analysis.

-Use of barbadin to block β-arrestin/AP-2-dependent internalisation is both technically precise and mechanistically informative

-Well-executed electrophysiology

-translation relevance

-converges to a model that peers discussed (scientists can only discuss models - not data!)

Weaknesses:

Nevertheless, the manuscript currently reads as a sequence of discrete experiments rather than a single causal chain.

Author response:

The following is the authors’ response to the previous reviews

Reviewer # 1 (Public review)

This study aims to elucidate the mechanisms by which stress-induced α2A-adrenergic receptor (α2A-AR) internalization leads to cytosolic noradrenaline (NA) accumulation and subsequent neuronal dysfunction in the locus coeruleus (LC). While the manuscript presents an interesting but ambitious model involving calcium dynamics, GIRK channel rundown, and autocrine NA signaling, several key limitations undermine the strength of the conclusions. 

(1) First, the revision does not include new experiments requested by reviewers to validate core aspects of the mechanism. Specifically, there is no direct measurement of cytosolic NA levels or MAO-A enzymatic activity to support the link between receptor internalization and neurochemical changes. The authors argue that such measurements are either not feasible or beyond the scope of the study, leaving a significant gap in the mechanistic chain of evidence. 

Although the reviewer #1 commented that “The authors argue that such measurements are either not feasible or beyond the scope of the study, leaving a significant gap in the mechanistic chain of evidence”, we believe that this comment may be unfair. 

It may be unfair for the reviewer #1 to neglect our responses to the original reviewer comments regarding the direct measurement of cytosolic NA levels. It is true that none of the recommended methods to directly measure cytosolic NA levels are not feasible as described in the original authors’ response (see the original authors’ response to the comment raised by the Reviewer #1 as Recommendations for the authors (2)). To measure extracellular NA with GRAB-NE photometry, α2A-ARs must be expressed in the cell membrane. GRAB-NE photometry is not applicable unless α2A-ARs are expressed, whereas increases in cytosolic NA levels are caused by internalization of α2A-ARs in our study.

In our study, we elaborated to detect the change in MAO-A protein with Western blot method, instead of examining MAO-A enzymatic activity. Because the relative quantification of active AEP and Tau N368 proteins by Western blot analysis should accurately reflect the change in the MAO-A enzymatic activity, enzymatic assay may not be necessarily required while we admit the necessity of enzymatic assay to better demonstrate the MAO-A activities as discussed in the previously revised manuscript (R1, page 10, lines 314-315). 

We used the phrase “beyond the scope of the current study” for “the mechanism how Ca²⁺ activates MAO-A” as described in the original authors’ responses (see the original authors’ response to the comment raised by the Reviewer #1 as Weakness (3)). We do not think that this mechanism must be investigated in the present study because the Ca²⁺ dependent nature of MAO-A activity is already known (Cao et al., 2007). 

On the other hand, because it is not possible to measure cytosolic NA levels with currently available methods, the quantification of the connection between α2A-AR internalization and increased cytosolic NA levels must be considered outside the scope of the study. However, our study demonstrated the qualitative relationship between α2A-AR internalization and active-AEP/TauN-368 reflecting increased cytosolic NA levels, leaving “a small gap in the mechanistic chain of evidence.” Therefore, it may be unreasonable to criticize our study as “leaving a significant gap in the mechanistic chain of evidence” with the phrase “beyond the scope of the current study.” 

(2) Second, the behavioral analysis remains insufficient to support claims of cognitive impairment. The use of a single working memory test following an anxiety test is inadequate to verify memory dysfunction behaviors. Additional cognitive assays, such as the Morris Water Maze or Novel Object Recognition, are recommended but not performed.

As described in the original authors’ response (see the original authors’ response to the comment raised by the Reviewer #1 as Weakness (4)), we had already done another behavioral test using elevated plus maze (EPM) test. By combining the two tests, it may be possible to more accurately evaluate the results of Y-maze test by differentiating the memory impairment from anxiety. However, the results obtained by these behavioral tests showed that chronic RS mice displayed both anxiety-like and memory impairment-like behaviors. Accordingly, we have softened the implication of anxiety and memory impairment (page 13, lines 396-399) and revised the abstract (page 2, line 59) in the revised manuscript (R2).  

(3) Third, concerns regarding the lack of rigor in differential MAO-A expression in fluorescence imaging were not addressed experimentally. Instead of clarifying the issue, the authors moved the figure to supplementary data without providing further evidence (e.g., an enzymatic assay or quantitative reanalysis of Western blot, or re-staining of IF for MAO-A) to support their interpretation.

Because the quantification of MAO-A expression can be performed with greater accuracy by means of Western blot than by immunohistochemistry, we have moved the immunohistochemical results (shown in Figure 5) to the supplemental data (Figure S8) following the suggestion made by the Reviewer #3. As the relative quantification of active AEP and Tau N368 proteins by Western blot analysis may accurately reflect changes in the MAO-A enzymatic activity which is consistent with the result of Western blot analysis of MAO-A, enzymatic assay or re-staining of immunofluorescence for MAO-A may not be necessarily required. We do not think that a new experiment of Western blot analysis is necessary to re-evaluate MAO-A just because of the lack of the less-reliable quantification of immunohistochemical staining.

(4) Fourth, concerns regarding TH staining remain unresolved. In Figure S7, the α2A-AR signal appears to resemble TH staining, and vice versa, raising the possibility of labeling errors. It is recommended that the authors re-examine this issue by either double-checking the raw data or repeating the immunostaining to validate the staining.

The reviewer #3 is misunderstanding Figure S7. In Figure S7, there are two types of α2A-AR expressing neurons; one is TH-positive LC neuron and the other is TH-negative neuron in mesencephalic trigeminal nucleus (MTN). This clearly indicates that TH staining is specific. Furthermore, α2A-AR staining was much more extensive in MTN neurons than in LC neurons. Thus, α2A-AR signal is not similar to TH signal and there are no labeling errors, which is also evident in the merged image (Figure S7C).

(5) Overall, the manuscript offers a potentially interesting framework but falls short in providing the experimental rigor necessary to establish causality. The reliance on indirect reasoning and reorganizing of existing data, rather than generating new evidence, limits the overall impact and interpretability of the study.

Overall, the reviewer #1 was not satisfied with our revision regardless of the authors’ responses. As detailed above in our responses to the replies (1)~(4), we believe that in the original authors’ responses and in the above-described responses we effectively responded to the criticisms by the reviewer #1.

Reviewer #2 (Public review): 

Comments on revisions: 

The authors have addressed all of the reviewers' comments.

We appreciate constructive and helpful comments made by the reviewer #2.

Reviewer #3 (Public review): 

Weaknesses:  

Nevertheless, the manuscript currently reads as a sequence of discrete experiments rather than a single causal chain. Below, I outline the key points that should be addressed to make the model convincing.

Please see the responses to the recommendation for the authors made by reviewer #3.

Reviewer #3 (Recommendations for the authors):

(1) Causality across the pathway  

Each step (α2A internalisation, GIRK rundown, Ca²⁺ rise, MAO-A/AEP upregulation) is demonstrated separately, but no experiment links them in a single preparation. Consider in vivo Ca²⁺ or GRAB NE photometry during restraint stress while probing α2A levels with i.p. clonidine injection or optogenetic over excitation coupled to biochemical readouts. Such integrated evidence would help to overcome the correlational nature of the manuscript to a more mechanistic study. 

Authors response: It is not possible to measure free cytosolic NA levels with GRAB NE photometry when α2A AR is internalized as described above (see the response to the comment made by reviewer #1 as the recommendation for the authors).

The core idea behind my comment, as well as that of Reviewer 1, was to encourage integrating your individual findings into a more cohesive in vivo experiment. Using GRAB-NE to measure extracellular NA could serve as an indirect readout of NA uptake via NAT, and ultimately, cytosolic NA levels. Connecting these experiments would significantly strengthen the manuscript and enhance its overall impact. 

It may be true that the measurement of extracellular NA could serve as an indirect readout of NA uptake via NAT, and ultimately cytosolic NA levels. However, the reviewer #3 is still misunderstanding the applicability of GRAB-NE method to detect NE in our study. As described in the original authors’ response, there appeared to be no fluorescence probe to label cytosolic NA at present. Especially, the GRAB-NE method recommended by the reviewers #1 and #3 is limited to detect NA only when α2A-AR is expressed in the cell membrane.Therefore, when increases in cytosolic NA levels are caused by internalization of α2A-ARs, NA measurement with GRAB-NE photometry is not applicable.

(2) Pharmacology and NE concentration  

The use of 100 µM noradrenaline saturates α and β adrenergic receptors alike. Please provide ramp measurements of GIRK current in dose-response at 1-10 µM NE (blocked by atipamezole) to confirm that the rundown really reflects α2A activity rather than mixed receptor effects. 

Authors response: It is true that 100 µM noradrenaline activates both α and β adrenergic receptors alike. However, it was clearly showed that enhancement of GIRK-I by 100 µM noradrenaline was completely antagonized by 10 µM atipamezole and the Ca²⁺ dependent rundown of NA-induced GIRK-I was prevented by 10 µM atipamezole. Considering the Ki values of atipamezole for α2A AR (=1~3 nM) (Vacher et al., 2010, J Med Chem) and β AR (>10 µM) (Virtanen et al., 1989, Arch Int Pharmacodyn Ther), these results really reflect α2A AR activity but not β AR activity (Figure S5). Furthermore, because it is already well established that NA-induced GIRK-I was mediated by α2A AR activity in LC neurons (Arima et al., 1998, J Physiol; Williams et al., 1985, Neuroscience), it is not necessarily need to re-examine 1-10 µM NA on GIRK-I.

While the milestone papers by Williams remain highly influential, they should be re-evaluated in light of more recent findings, given that they date back over 40 years. Advances in our understanding now allow for a more nuanced interpretation of some of their results. For example, see McKinney et al. (eLife, 2023). This study demonstrates that presynaptic β-adrenergic receptors-particularly β2-can enhance neuronal excitability via autocrine mechanisms. This suggests that your post-activation experiments using atipamezole may not fully exclude a contribution of β-adrenergic signaling. Such a role might become apparent when conducting more detailed titration experiments.

The reviewer #3 may be misunderstanding the report by McKinney et al. (eLife, 2013). This paper did not demonstrate that presynaptic β-adrenergic receptors-particularly β2- can enhance neuronal excitability via autocrine mechanisms. It is impossible for LC neurons to increase their excitability by activating β-adrenergic receptors, as we have clearly shown that enhancement of GIRK-I by 100 µM noradrenaline was completely antagonized by 10 µM atipamezole. Considering the difference in Ki values of atipamezole for α2-AR (= 2~4 nM) (Vacher et al., 2010, J Med Chem) and β-AR (>10 µM) (Virtanen et al., 1989, Arch Int Pharmacodyn Ther), such a complete antagonization (of 100 µM NA-induced GIRK-I) by 10 µM atipamezole really reflect α2A-AR activity but not β-AR activity (Figure S5). Furthermore, it is already well established that NA-induced GIRK-I was mediated by α2-AR activity in LC neurons (Arima et al., 1998, J Physiol). McKinney et al. (eLife, 2023) have just found the absence of lateral inhibition on adjacent LC neurons by NA autocrine caused respective spike activity. This has nothing to do with autoinhibition.

(4) Age mismatch and disease claims 

All electrophysiology and biochemical data come from juvenile (< P30) mice, yet the conclusions stress Alzheimer-related degeneration. Key endpoints need to be replicated in adult or aged mice, or the manuscript should soften its neurodegenerative scope. 

Authors response: As described in the section of Conclusion, we never stress Alzheimer-related degeneration, but might give such an impression. To avoid such a misunderstanding, we have added a description “However, the present mechanism must be proven to be valid in adult or old mice, to validate its involvement in the pathogenesis of AD.” (R1, page 14, lines 448-450).

It would be great to see this experiment performed in aged mice-you are the one who has everything in place to do it right now! 

In our future separate studies, we would like to prove that the present mechanism is valid in aged mice, to validate its involvement in the pathogenesis of AD. This is partly because the patch-clamp study in aged mice is extremely difficult and takes much time.

Authors response: In the abstract, you suggest that internalization of α2A-adrenergic receptors could represent a therapeutic target for Alzheimer's disease. "...Thus, it is likely that internalization of α2A-AR increased cytosolic NA, as reflected in AEP increases, by facilitating reuptake of autocrine-released NA. The suppression of α2A-AR internalization may have a translational potential for AD treatment."

α2A-AR internalization was involved in the degeneration of LC neurons. Because we confirmed that spike-frequency adaptation reflecting α2A-AR-mediated autoinhibition can be induced in adult mice as prominently as in juvenile mice (Figure S10), it is not inadequate to suggest that the suppression of α2A-AR internalization may have a translational potential for anxiety/AD treatment (see Discussion; R2, page 14, lines 445-449).

(6) Quantitative histology  

Figure 5 presents attractive images, but no numerical analysis is provided. Please provide ROI-based fluorescence quantification (with n values) or move the images to the supplement and rely on the Western blots. 

Author response: We have moved the immunohistochemical results in Fig. 5 to the supplement, as we believe the quantification of immunohistochemical staining is not necessarily correct.   

What do you mean by that " ...immunohistochemical staining is not necessarily correct."  

It is evident that in terms of quantification, Western blot analysis is a more accurate method than immunohistochemical staining. In this sense, it is the contention of our study that the ROI-based fluorescence quantification of immunohistochemical staining is not necessarily an accurate or correct procedure, compared to the quantification by Western blot analysis.

cf spectale opera seeing being seen theatre, set des

infra: Silver/KE? on modernity? modularity and buildings as moldable assets

Is there any overlap in the performance of CBTs in comparison to other approaches to psychological treatments? CBT is so broadly used, but there is another form of therapy that performs similarly?

I can appreciate this perspective and initiative to make CBT something that is effective in not only western culture, but in minorities as well. This is rather interesting because there are a plethora of cultural differences that influence how we view and understand the world. I could imagine that these differences could also influence how we experience our thoughts, feelings, and emotions differently.

In relation, the techniques may not have to change completely, just adjusted based on the cultural background of the person.

This heavily affects other races even today although these circumstances started years ago.

Why is it that they are rarely tested?

One possible step forward could be developing standardized multicultural CBT training, modules, panels, and open room discussions that are required across graduate programs. This'll utilizing younger minds perspective on what can better connect the culture gap in CBT.

They shift from asking if cultural competency is needed to how to implement it but yet how will we actually define and measure culturally competent CBT? Without clear benchmarks and more useful tools, when can we tell when knowledge becomes real skills for said groups?

This part describes how clinician bias and racial microaggressions can lead to misdiagnosis, damage the therapeutic relationship, and discourage clients of color from seeking or continuing mental health care.

This passage explains the three main models of cultural competence and points out that adaptation models are the most frequently tested, while skills-based and process-oriented models remain under studied

This section highlights that CBT is generally effective across cultures but may work slightly less well for ethnic minorities, and it underscores a major research gap because very few trials test cultural sensitivity training for clinicians.

This is important to keep in mind because the land has been altered by human, and not only have resources been added, but policies have also been put in place to regulate its use

eLife Assessment

This study presents a sequence-based method for predicting drug-interacting residues in intrinsically disordered proteins (IDPs), addressing a significant challenge in understanding small-molecule:IDP interactions. The findings have solid support through examples underscoring the role of aromatic interactions. While predicted binding sites remain coarse, validation was done on a total of 10 IDPs at varying depths. The method builds on the authors' previous work and, with ad hoc modifications, is poised to benefit this emerging field.

Reviewer #1 (Public review):

Summary:

The authors developed a sequence-based method to predict drug-interacting residues in IDP, based on their recent work, to predict the transverse relaxation rates (R2) of IDP trained on 45 IDP sequences and their corresponding R2 values. The discovery is that the IDPs interact with drugs mostly using aromatic residues that are easy to understand, as most drugs contain aromatic rings. They validated the method using several case studies, and the predictions are in accordance with chemical shift perturbations and MD simulations. The location of the predicted residues serves as a starting point for ligand optimization.

Strengths:

This work provides the first sequence-based prediction method to identify potential drug-interacting residues in IDP. The validity of the method is supported by case studies. It is easy to use, and no time-consuming MD simulations and NMR studies are needed.

Weaknesses:

The method does not depend on the information of binding compounds, which may give general features of IDP-drug binding. However, due to the size and chemical structures of the compounds (for example, how many aromatic rings), the number of interacting residues varies, which is not considered in this work. Lacking specific information may restrict its application in compound optimization, aiming to derive specific and potent binding compounds.

Comments on revised version:

I'm satisfied with the authors' response and the public review does not need further changes.

Reviewer #2 (Public review):

Summary:

In this work, the authors introduce DIRseq, a fast, sequence-based method that predicts drug-interacting residues (DIRs) in IDPs without requiring structural or drug information. DIRseq builds on the authors' prior work looking at NMR relaxation rates, and presumes that those residues that show enhanced R2 values are the residues that will interact with drugs, allowing these residues to be nominated from the sequence directly. By making small modifications to their prior tool, DIRseq enables the prediction of residues seen to interact with small molecules in vivo.

Strengths:

The preprint is well written and easy to follow.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

The authors developed a sequence-based method to predict drug-interacting residues in IDP, based on their recent work, to predict the transverse relaxation rates (R2) of IDP trained on 45 IDP sequences and their corresponding R2 values. The discovery is that the IDPs interact with drugs mostly using aromatic residues that are easy to understand, as most drugs contain aromatic rings. They validated the method using several case studies, and the predictions are in accordance with chemical shift perturbations and MD simulations. The location of the predicted residues serves as a starting point for ligand optimization.

Strengths:

This work provides the first sequence-based prediction method to identify potential druginteracting residues in IDP. The validity of the method is supported by case studies. It is easy to use, and no time-consuming MD simulations and NMR studies are needed.

Weaknesses:

The method does not depend on the information of binding compounds, which may give general features of IDP-drug binding. However, due to the size and chemical structures of the compounds (for example, how many aromatic rings), the number of interacting residues varies, which is not considered in this work. Lacking specific information may restrict its application in compound optimization, aiming to derive specific and potent binding compounds.

We fully recognize that different compounds may have different interaction propensity profiles along the IDP sequence. In future studies, we will investigate compound-specific parameter values. The limiting factor is training data, but such data are beginning to be available.

Reviewer #2 (Public review):

Summary:

In this work, the authors introduce DIRseq, a fast, sequence-based method that predicts druginteracting residues (DIRs) in IDPs without requiring structural or drug information. DIRseq builds on the authors' prior work looking at NMR relaxation rates, and presumes that those residues that show enhanced R2 values are the residues that will interact with drugs, allowing these residues to be nominated from the sequence directly. By making small modifications to their prior tool, DIRseq enables the prediction of residues seen to interact with small molecules in vivo.

Strengths:

The preprint is well written and easy to follow

Weaknesses:

(1) The DIRseq method is based on SeqDYN, which itself is a simple (which I do not mean as a negative - simple is good!) statistical predictor for R2 relaxation rates. The challenge here is that R2 rates cover a range of timescales, so the physical intuition as to what exactly elevated R2 values mean is not necessarily consistent with "drug interacting". Presumably, the authors are not using the helix boost component of SeqDYN here (it would be good to explicitly state this). This is not necessarily a weakness, but I think it would behove the authors to compare a few alternative models before settling on the DIRseq method, given the somewhat ad hoc modifications to SeqDYN to get DIRseq.

Actually, the factors that elevate R2 are well-established. These are local interactions and residual secondary structures (if any). The basic assumption of our method is that intra-IDP interactions that elevate R2 convert to IDP-drug interactions. This assumption was supported by our initial observation that the drug interaction propensity profiles predicted using the original SeqDYN parameters already showed good agreement with CSP profiles. We only made relatively small adjustments to the parameters to improve the agreement. Indeed we did not apply the helix boost portion of SeqDYN to DIRseq, and now state as such (p. 4, second last paragraph). We now also compare DIRseq with several alternative models, as summarized in new Table S2.

Specifically, the authors previously showed good correlation between the stickiness parameter of Tesei et al and the inferred "q" parameter for SeqDYN; as such, I am left wondering if comparable accuracy would be obtained simply by taking the stickiness parameters directly and using these to predict "drug interacting residues", at which point I'd argue we're not really predicting "drug interacting residues" as much as we're predicting "sticky" residues, using the stickiness parameters. It would, I think, be worth the authors comparing the predictive power obtained from DIRseq with the predictive power obtained by using the lambda coefficients from Tesei et al in the model, local density of aromatic residues, local hydrophobicity (note that Tesei at al have tabulated a large set of hydrophobicity scores!) and the raw SeqDYN predictions. In the absence of lots of data to compare against, this is another way to convince readers that DIRseq offers reasonable predictive power.

We now compare predictions of these various parameter sets, and report the results in Table S2.  In short, among all the tested parameter sets, DIRseq has the best performance as measured by (1) strong correlations between prediction scores and CSPs and (2) high true positives and low false positives (p. 7-9).

(2) Second, the DIRseq is essentially SeqDYN with some changes to it, but those changes appear somewhat ad hoc. I recognize that there is very limited data, but the tweaking of parameters based on physical intuition feels a bit stochastic in developing a method; presumably (while not explicitly spelt out) those tweaks were chosen to give better agreement with the very limited experimental data (otherwise why make the changes?), which does raise the question of if the DIRseq implementation of SeqDYN is rather over-parameterized to the (very limited) data available now? I want to be clear, the authors should not be critiqued for attempting to develop a model despite a paucity of data, and I'm not necessarily saying this is a problem, but I think it would be really important for the authors to acknowledge to the reader the fact that with such limited data it's possible the model is over-fit to specific sequences studied previously, and generalization will be seen as more data are collected.

We have explained the rationale for the parameter tweaks, which were limited to q values for four amino-acid types, i.e., to deemphasize hydrophobic interactions and slightly enhance electrostatic interactions (p. 4-5). We now add that these tweaks were motivated by observations from MD simulations of drug interactions with a-syn (ref 13). As already noted in the response to the preceding comment, we now also present results for the original parameter values as well as for when the four q values are changed one at a time.

(3) Third, perhaps my biggest concern here is that - implicit in the author's assumptions - is that all "drugs" interact with IDPs in the same way and all drugs are "small" (motivating the change in correlation length). Prescribing a specific length scale and chemistry to all drugs seems broadly inconsistent with a world in which we presume drugs offer some degree of specificity. While it is perhaps not unexpected that aromatic-rich small molecules tend to interact with aromatic residues, the logical conclusion from this work, if one assumes DIRseq has utility, is that all IDRs bind drugs with similar chemical biases. This, at the very least, deserves some discussion.

The reviewer raises a very important point. In Discussion, we now add that it is important to further develop DIRseq to include drug-specific parameters when data for training become available (p. 12-13). To illustrate this point, we use drug size as a simple example, which can be modeled by making the b parameter dependent on drug molecule size.

(4) Fourth, the authors make some general claims in the introduction regarding the state of the art, which appear to lack sufficient data to be made. I don't necessarily disagree with the author's points, but I'm not sure the claims (as stated) can be made absent strong data to support them. For example, the authors state: "Although an IDP can be locked into a specific conformation by a drug molecule in rare cases, the prevailing scenario is that the protein remains disordered upon drug binding." But is this true? The authors should provide evidence to support this assertion, both examples in which this happens, and evidence to support the idea that it's the "prevailing view" and specific examples where these types of interactions have been biophysically characterized.

We now cite nine studies showing that IDPs remain disordered upon drug binding.

Similarly, they go on to say:

"Consequently, the IDP-drug complex typically samples a vast conformational space, and the drug molecule only exhibits preferences, rather than exclusiveness, for interacting with subsets of residues." But again, where is the data to support this assertion? I don't necessarily disagree, but we need specific empirical studies to justify declarative claims like this; otherwise, we propagate lore into the scientific literature. The use of "typically" here is a strong claim, implying most IDP complexes behave in a certain way, yet how can the authors make such a claim? 

Here again we add citations to support the statement.

Finally, they continue to claim:

"Such drug interacting residues (DIRs), akin to binding pockets in structured proteins, are key to optimizing compounds and elucidating the mechanism of action." But again, is this a fact or a hypothesis? If the latter, it must be stated as such; if the former, we need data and evidence to support the claim.

We add citations to both compound optimization and mechanism of action.

Reviewer #1 (Recommendations for the authors):

(1) The authors should compare the sequences of the IDPs in the case studies with the 45 IDPs in training the SeqDYN model to make sure that they are not included in the training dataset or are highly homologous.

Please note that the data used for training SeqDYN were R2 rates, which are independent of the property being studied here, i.e., drug interacting residues. Therefore whether the IDPs studied here were in the training set for SeqDYN is immaterial.

(2) The authors manually tuned four parameters in SeqDYN to develop the model for predicting drug-interacting residues without giving strict testing or explanations. More explanations, testing of more values, and ablation testing should be given.

As responded above, we now both expand the explanation and present more test results.

(3) The authors changed the q values of L, I, and M to the value of V. What are the results if these values are not changed?

These results are shown in Table S2 (entry named SeqDYN_orig).

(4) Only one b value is chosen based on the assumption that a drug molecule interacts with 3-4 residues at a time. However, the number of interacting residues is related to the size of the drug molecule. Adjusting the b value with the size of the ligand may provide improvement. It is better to test the influence of adjusting b values. At least, this should be discussed.

Good point! We now state that b potentially can be adjusted according to ligand size (p. 12-13). In addition, we also show the effect of varying b on the prediction results (Table S2; p. 8, last paragraph).

(5) The authors add 12 Q to eliminate end effects. However, explanations on why 12 Qs are chosen should be given. How about other numbers of Q or using other residues (e.g., the commonly used residues in making links, like GS/PS or A?

As we already explained, “Gln was selected because its 𝑞 value is at the middle of the 20 𝑞 values.” (p. 5, second paragraph). Also, 12 Qs are sufficient to remove any end effects; a higher number of Qs does not make any difference.

Reviewer #2 (Recommendations for the authors):

(1) The authors make reference to the "C-terminal IDR" in cMyc, but the region they note is found in the bHLH DNA binding domain (which falls from residue ~370-420).

We now clarify that this region is disordered on its own but form a helix-loop-loop structure upon heterodimerization with Max (p. 11, last paragraph).

(2) Given the fact that X-seq names are typically associated with sequencing-based methods, it's perhaps confusing to name this method DIRseq?

We appreciate the reviewer’s point, but by now the preprint posted in bioRxiv is in wide circulation, and the DIRseq web server has been up for several months, so changing its name would cause a great deal of confusion.

(3) I'd encourage the authors just to spell out "drug interacting residues" and retain an IDR acronym for IDRs. Acronyms rarely make writing clearer, and asking folks to constantly flip between IDR and DIR is asking a lot of an audience (in this reviewer's opinion, anyway).

The reviewer makes a good point; we now spell out “drug-interacting residues”.

(4) The assumption here is that CSPs result from direct drug:IDR interactions. However, CSPs result from a change in the residue chemical environment, which could in principle be an indirect effect (e.g., in the unbound state, residues A and B interact; in the bound state, residue A is now free, such that it experiences a CSP despite not engaging directly). While I recognize such assumptions are commonly made, it behoves the authors to explicitly make this point so the reader understands the relationship between CSPs and binding.

We did add caveats of CSP in Introduction (p. 3, second paragraph).

(5) On the figures, please label which protein is which figure, as well as provide a legend for the annotations on the figures (red line, blue bar, cyan region, etc.)

We now label protein names in Fig. 1. For annotation of display items, it is also made in the Figs. 2 and 3 captions; we now add it to the Fig. 4 caption.

(6) abstract: "These successes augur well for deciphering the sequence code for IDP-drug binding." - This is not grammatically correct, even if augur were changed to agree. Suggest rewriting.

“Augur well” means to be a good sign (for something). We use this phrase here in this meaning.

(6) page 5: "we raised the 𝑞 value of Asp to be the same as that of Glu" → suggested "increased" instead of raised.

We have made the suggested change.

(7) The authors should consider releasing the source code (it is available via the .js implementation on the server, but this is not very transferable/shareable, so I'd encourage the authors to provide a stand-alone implementation that's explicitly shareable).

We have now added a link for the user to download the source code.

The absolute last thing I want here is to delegate decisions about interaction style and the implementation of application-level affordances to the person supplying the content.

Asking him to stop favouring men

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eLife Assessment

This important study provides new insights into the lesser-known effects of the sodium-potassium pump on how nerve cells process signals, particularly in highly active cells like those of weakly electric fish. The computational methods used to establish the claims in this work are compelling and can be used as a starting point for further studies.

Reviewer #1 (Public review):

Summary:

The authors aim to explore the effects of the electrogenic sodium-potassium pump (Na+/K+-ATPase) on the computational properties of highly active spiking neurons, using the weakly-electric fish electrocyte as a model system. Their work highlights how the pump's electrogenicity, while essential for maintaining ionic gradients, introduces challenges in neuronal firing stability and signal processing, especially in cells that fire at high rates. The study identifies compensatory mechanisms that cells might use to counteract these effects, and speculates on the role of voltage dependence in the pump's behavior, suggesting that Na+/K+-ATPase could be a factor in neuronal dysfunctions and diseases

Strengths:

(1) The study explores a less-examined aspect of neural dynamics-the effects of Na+/K+-ATPase electrogenicity. It offers a new perspective by highlighting the pump's role not only in ion homeostasis but also in its potential influence on neural computation.

(2) The mathematical modeling used is a significant strength, providing a clear and controlled framework to explore the effects of the Na+/K+-ATPase on spiking cells. This approach allows for the systematic testing of different conditions and behaviors that might be difficult to observe directly in biological experiments.

(3) The study several interesting compensatory mechanisms, such as sodium leak channels and extracellular potassium buffering, which provide useful theoretical frameworks for understanding how neurons maintain firing rate control despite the pump's effects.

Comments on revisions:proposes

The revised manuscript is notably improved.

Reviewer #2 (Public review):

Summary:

The paper by Weerdmeester, Schleimer, and Schreiber uses computational models to present the biological constraints under which electrocytes - specialized, highly active cells that facilitate electro-sensing in weakly electric fish-may operate. The authors suggest potential solutions that these cells could employ to circumvent these constraints.

Electrocytes are highly active or spiking (greater than 300Hz) for sustained periods (for minutes to hours), and such activity is possible due to an influx of sodium and efflux of potassium ions into these cells after each spike. The resulting ion imbalance must be restored, which in electrocytes, as with many other biological cells, is facilitated by the Na-K pumps at the expense of biological energy, i.e., ATP molecules. For each ATP molecule the pump uses, three positively charged sodium ions from the intracellular space are exchanged for two positively charged potassium ions from the extracellular space. This creates a net efflux of positive ions into the extracellular space, resulting in hyperpolarized potentials for the cell over time. For most cells, this does not pose an issue, as their firing rate is much slower, and other compensatory mechanisms and pumps can effectively restore the ion imbalances. However, in the electrocytes of weakly electric fish, which spike at exceptionally high rates, the net efflux of positive ions presents a challenge. Additionally, these cells are involved in critical communication and survival behaviors, underscoring their essential role in reliable functioning.

In a computational model, the authors test four increasingly complex solutions to the problem of counteracting the hyperpolarized states that occur due to continuous NaK pump action to sustain baseline activity. First, they propose a solution for a well-matched Na leak channel that operates in conjunction with the NaK pump, counteracting the hyperpolarizing states naturally. Their model shows that when such an orchestrated Na leak current is not included, quick changes in the firing rates could have unexpected side effects. Secondly, they study the implications of this cell in the context of chirps-a means of communication between individual fish. Here, an upstream pacemaking neuron entrains the electrocyte to spike, which ceases to produce a so-called chirp - a brief pause in the sustained activity of the electrocytes. In their model, the authors demonstrate that including the extracellular potassium buffer is necessary to obtain a reliable chirp signal. Thirdly, they tested another means of communication in which there was a sudden increase in the firing rate of the electrocyte, followed by a decay to the baseline. For this to occur reliably, the authors emphasize that a strong synaptic connection between the pacemaker neuron and the electrocyte is necessary. Finally, since these cells are energy-intensive, they hypothesize that electrocytes may have energy-efficient action potentials, for which their NaK pumps may be sensitive to the membrane voltages and perform course correction rapidly.

Strengths:

The authors extend an existing electrocyte model (Joos et al., 2018) based on the classical Hodgkin and Huxley conductance-based models of sodium and potassium currents to include the dynamics of the sodium-potassium (NaK) pump. The authors estimate the pump's properties based on reasonable assumptions related to the leak potential. Their proposed solutions are valid and may be employed by weakly electric fish. The authors explore theoretical solutions to electrosensing behavior that compound and suggest that all these solutions must be simultaneously active for the survival and behavior of the fish. This work provides a good starting point for conducting in vivo experiments to determine which of these proposed solutions the fish employ and their relative importance. The authors include testable hypotheses for their computational models.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

The authors aim to explore the effects of the electrogenic sodium-potassium pump (Na<SUP>+</SUP>/K<SUP>+</SUP>ATPase) on the computational properties of highly active spiking neurons, using the weakly-electric fish electrocyte as a model system. Their work highlights how the pump's electrogenicity, while essential for maintaining ionic gradients, introduces challenges in neuronal firing stability and signal processing, especially in cells that fire at high rates. The study identifies compensatory mechanisms that cells might use to counteract these effects, and speculates on the role of voltage dependence in the pump's behavior, suggesting that Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase could be a factor in neuronal dysfunctions and diseases

Strengths:

(1) The study explores a less-examined aspect of neural dynamics-the effects of Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase electrogenicity. It offers a new perspective by highlighting the pump's role not only in ion homeostasis but also in its potential influence on neural computation.

(2) The mathematical modeling used is a significant strength, providing a clear and controlled framework to explore the effects of the Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase on spiking cells. This approach allows for the systematic testing of different conditions and behaviors that might be difficult to observe directly in biological experiments.

(3) The study proposes several interesting compensatory mechanisms, such as sodium leak channelsand extracellular potassium buffering, which provide useful theoretical frameworks for understanding how neurons maintain firing rate control despite the pump's effects.

Weaknesses:

(1) While the modeling approach provides valuable insights, the lack of experimental data to validate the model's predictions weakens the overall conclusions.

(2)The proposed compensatory mechanisms are discussed primarily in theoretical terms without providing quantitative estimates of their impact on the neuron's metabolic cost or other physiological parameters.

Comments on revisions:

The revised manuscript is notably improved.

We thank the reviewer for their concise and accurate summary and appreciate the constructive feedback on the article’s strengths and weaknesses. Experimental work is beyond the scope of our modeling-based study. However, we would like our work to serve as a framework for future experimental studies into the role of the electrogenic pump current (and its possible compensatory currents) in disease, and its role in evolution of highly specialized excitable cells (such as electrocytes).

Quantitative estimates of metabolic costs in this study are limited to the ATP that is required to fuel the Na<SUP>+</SUP>/K<SUP>+</SUP> pump. By integrating the net pump current over time and dividing by one elemental charge, one can find the rate of ATP that is consumed by the Na<SUP>+</SUP>/K<SUP>+</SUP> pump for either compensatory mechanism. The difference in net pump current is thus proportional to ATP consumption, which allows for a direct comparison of the cost efficiency of the Na<SUP>+</SUP>/K<SUP>+</SUP> pump for each proposed compensatory mechanism. The Na<SUP>+</SUP>/K<SUP>+</SUP> pump is however not the only ATP-consuming element in the electrocyte, and some of the compensatory mechanisms induce other costs related to cell ‘housekeeping’ or presynaptic processes. We now added a section in the appendix titled ‘Considerations on metabolic costs of compensatory mechanisms’ (section 11.4), where we provide rough estimates on the influence of the compensatory mechanisms on the total metabolic costs of the cell and membrane space occupation. Although we argue that according these rough estimates, the impact of discussed compensatory mechanisms could be significant, due to the absence of more detailed experimental quantification, a plausible quantitative cost estimate on the whole cell level remains beyond the scope of this article.

Reviewer #1 (Recommendations for the authors):

I just have a few recommendations on the updated manuscript.

(1) When exploring the different roles of Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase in the Results section, the authors employed many different models. For instance, the voltage equation on page 15, voltage equation (2) on page 22, voltage equation (12) on page 24, voltage equation (30) on page 32, and voltage equation (38) on page 35 are presented as the master equations for their respective biophysical models. Meanwhile, the phase models are presented on page 29 and page 33. I would recommend that the authors clearly specify which equations correspond to each subsection of the Results section and explicitly state which equations were used to generate the data in each figure. This would help readers more easily follow the connections between the models, the results, and the figures.

We thank the reviewer for pointing out that the links of the different voltage equations to the results could be expressed more explicitly in the article. All simulations were done using the ‘master equation’  expressed in Eq. 2, and the other voltage equations that are specified in the article (in the new version of the article Eqs. 13, 31, and 39) are reformulations of Eq. 2 to analytically show different properties of the voltage equation (Eq. 2). This has now been mentioned in the article when formulating the voltage equations, and the equation for the total leak current (in the new version Eq. 3) has been added for completeness.

(2) The authors may want to revisit their description and references concerning Eigenmannia virescens. For example, wave-type weakly electric fish (e.g., Eigenmannia) and pulse-type weakly electric fish (e.g., Gymnotus carapo) exhibit large differences, making references 52-55 may be inappropriate for subsection 4.3.1, as these studies focus on Gymnotus carapo. Additionally, even within wave-type species, chirp patterns vary. For example, Eigenmannia can exhibit short "pauses"-type chirps, whereas Apteronotus leptorhynchus (another waver-form fish) does not (https://pubmed.ncbi.nlm.nih.gov/14692494/).

We thank the reviewer for pointing this out. The citations and phrasing in sections 4.3.1 and 4.3.2 have been updated to specifically refer to the weakly electric fish e. Virescens.

(3) Table on page 21: Please explain why the parameter value (13.5mM) of [Na<SUP>^</SUP>+]_{in} is 10 timeslarger than its value (1.35mM) in reference [26]? How does this value (13.5mM) compare with the range of variable [Na<SUP>^</SUP>+]_{in} in equation (6)?

The intracellular sodium concentration in reference [26] was reported to be 1.35 mM, but the authors also reported an extracellular sodium concentration of 120 mM, and a sodium reversal potential of 55 mV. Upon calculating the sodium reversal potential, we found that an intracellular sodium concentration of 1.35 mM would give a sodium reversal potential of 113 mV. An intracellular sodium concentration of 13.5 mM, on the other hand, leads to the reported and physiological reversal potential of 55 mV. This has now been clarified in the article, and the connection between this value and Eq. 6 (Eq. 7 in the new version) has also been clarified.

Reviewer #2 (Public review):

Summary:

The paper by Weerdmeester, Schleimer, and Schreiber uses computational models to present the biological constraints under which electrocytes - specialized, highly active cells that facilitate electro-sensing in weakly electric fish-may operate. The authors suggest potential solutions that these cells could employ to circumvent these constraints.

Electrocytes are highly active or spiking (greater than 300Hz) for sustained periods (for minutes to hours), and such activity is possible due to an influx of sodium and efflux of potassium ions into these cells after each spike. The resulting ion imbalance must be restored, which in electrocytes, as with many other biological cells, is facilitated by the Na-K pumps at the expense of biological energy, i.e., ATP molecules. For each ATP molecule the pump uses, three positively charged sodium ions from the intracellular space are exchanged for two positively charged potassium ions from the extracellular space. This creates a net efflux of positive ions into the extracellular space, resulting in hyperpolarized potentials for the cell over time. For most cells, this does not pose an issue, as their firing rate is much slower, and other compensatory mechanisms and pumps can effectively restore the ion imbalances. However, in the electrocytes of weakly electric fish, which spike at exceptionally high rates, the net efflux of positive ions presents a challenge. Additionally, these cells are involved in critical communication and survival behaviors, underscoring their essential role in reliable functioning.

In a computational model, the authors test four increasingly complex solutions to the problem of counteracting the hyperpolarized states that occur due to continuous NaK pump action to sustain baseline activity. First, they propose a solution for a well-matched Na leak channel that operates in conjunction with the NaK pump, counteracting the hyperpolarizing states naturally. Their model shows that when such an orchestrated Na leak current is not included, quick changes in the firing rates could have unexpected side effects. Secondly, they study the implications of this cell in the context of chirps-a means of communication between individual fish. Here, an upstream pacemaking neuron entrains the electrocyte to spike, which ceases to produce a so-called chirp - a brief pause in the sustained activity of the electrocytes. In their model, the authors demonstrate that including the extracellular potassium buffer is necessary to obtain a reliable chirp signal. Thirdly, they tested another means of communication in which there was a sudden increase in the firing rate of the electrocyte, followed by a decay to the baseline. For this to occur reliably, the authors emphasize that a strong synaptic connection between the pacemaker neuron and the electrocyte is necessary. Finally, since these cells are energy-intensive, they hypothesize that electrocytes may have energy-efficient action potentials, for which their NaK pumps may be sensitive to the membrane voltages and perform course correction rapidly.

Strengths:

The authors extend an existing electrocyte model (Joos et al., 2018) based on the classical Hodgkin and Huxley conductance-based models of sodium and potassium currents to include the dynamics of the sodium-potassium (NaK) pump. The authors estimate the pump's properties based on reasonable assumptions related to the leak potential. Their proposed solutions are valid and may be employed by weakly electric fish. The authors explore theoretical solutions to electrosensing behavior that compound and suggest that all these solutions must be simultaneously active for the survival and behavior of the fish. This work provides a good starting point for conducting in vivo experiments to determine which of these proposed solutions the fish employ and their relative importance. The authors include testable hypotheses for their computational models.

Weaknesses:

The model for action potential generation simplifies ion dynamics by considering only sodium and potassium currents, excluding other ions like calcium. The ion channels considered are assumed to be static, without any dynamic regulation such as post-translational modifications. For instance, a sodium-dependent potassium pump could modulate potassium leak and spike amplitude (Markham et al., 2013).

This work considers only the sodium-potassium (NaK) pumps to restore ion gradients. However, in many cells, several other ion pumps, exchangers, and symporters are simultaneously present and actively participate in restoring ion gradients. When sodium currents dominate action potentials, and thus when NaK pumps play a critical role, such as the case in Eigenmannia virescens, the present study is valid. However, since other biological processes may find different solutions to address the pump's non-electroneutral nature, the generalizability of the results in this work to other fast-spiking cell types is limited. For example, each spike could include a small calcium ion influx that could be buffered or extracted via a sodium-calcium exchanger.

We thank the reviewer for the detailed summary and the updated identified strengths and weaknesses. The current article indeed focuses on and isolates the interplay between sodium currents, potassium currents, and sodium-potassium pump currents. As discussed in section 5.1, in excitable cells where these currents are the main players in action-potential generation, the results presented in this article are applicable. The contribution of post-translational effects of ion channels, other ionic currents, and other active transporters and pumps, could be exciting avenues for further studies

.

Reviewer #2 (Recommendations for the authors):

Thank you for addressing my comments.

All the figures are now consistent. The color schema used is clear.

The methods and discussions expansions improve the paper.

Including the model assumptions and simplifications is appreciated.

Including internal references is helpful.

The equations are clear, and the references have been fixed.

I am content with the changes. I have updated my review accordingly.

We thank the reviewer for their initial constructive comments that lead to the significant improvement of the article.

Page : 3 Line : 113 Author : Unknown Author 07/24/2025 

Although this is technically correct, the article is about electrocommunication signals and does not focus on sensing.

Page : 3 Line : 153 Author : Unknown Author 07/24/2025

electrocommunication

Page : 4 Line : 164 Author : Unknown Author 07/24/2025 

Judging from the cited article, I think this should be a sodium-dependent potassium current.

eLife Assessment

The analysis of neural morphology across Heliconiini butterfly species revealed brain area-specific changes associated with new foraging behaviours. While the volume of the centre for learning and memory, the mushroom bodies, was known to vary widely across species, new, valuable results show conservation of the volume of a center for navigation, the central complex. The presented evidence is convincing for both volumetric conservation in the central complex and fine neuroanatomical differences associated with pollen feeding, delivered by experimental approaches that are applicable to other insect species. This work will be of interest to evolutionary biologists, entomologists, and neuroscientists.

Reviewer #1 (Public review):

The authors previously reported that Heliconius, one genus of the Heliconiini butterflies, evolved to be efficient foragers to feed pollen of specific plants and have massively expanded mushroom bodies. Using the same image dataset, the authors segmented the central complex and associated brain regions and found that the volume of the central complex relative to the rest of the brain is largely conserved across the Heliconiini butterflies. By performing immunostaining to label a specific subset of neurons, the authors found several potential sites of evolutionary divergence in the central complex neural circuits, including the number of GABAergic ellipsoid body ring neurons and the innervation patterns of Allatostatin A expressing neurons in the noduli. These neuroanatomical data will be helpful to guide future studies to understand the evolution of the neural circuits for vector-based navigation.

Strengths:

The authors used a sufficiently large scale of dataset from 307 individuals of 41 species of Heliconiini butterflies to solidify the quantitative conclusions and present new microscopy data for fine neuroanatomical comparison of the central complex.

Weaknesses:

(1) Although the figures display a concise summary of anatomical findings, it would be difficult for non-experts to learn from this manuscript to identify the same neuronal processes in the raw confocal stacks. It would be helpful to have instructive movies to show a step-by-step guide for identification of neurons of interest, segmentations, and 3D visualizations (rotation) for several examples, including ER neurons (to supplement texts in line 347-353) and Allatostatin A neurons.

(2) Related to (1), it was difficult for me to assess if the data in Figure 7 support the author's conclusions that ER neuron number increased in Heliconius Melpomene. By my understanding, the resolution of this dataset isn't high enough to trace individual axons and therefore authors do not rule out that the portion of "ER ring neurons" in Heliconius may not innervate the ER, as stated in Line 635 "Importantly, we also found that some ER neurons bypass the ellipsoid body and give rise to dense branches within distinct layers in the fan-shaped body (ER-FB)". If they don't innervate the ellipsoid body, why are they named as "ER neurons"?

(3) Discussions around the lines 577-584 require the assumption that each ellipsoid body (EB) ring neuron typically arborises in a single microglomerulus to form a largely one-to-one connection with TuBu neurons within the bulb (BU), and therefore, the number of BU microglomeruli should provide an estimation of the number of ER neurons. Explain this key assumption or provide an alternative explanation.

(4) The details of antibody information are missing in the Key resource table. Instead of citing papers, list the catalogue numbers and identifier for commercially available antibodies, and describe the antigen, and whether they are monoclonal or polyclonal. Are antigens conserved across species?

(5) I did not understand why authors assume that foraging to feed on pollens is a more difficult cognitive task than foraging to feed on nectar. Would it be possible that they are equally demanding tasks, but pollen feeding allows Heliconius to pass more proteins and nucleic acids to their offspring and therefore they can develop larger mushroom bodies?

Reviewer #2 (Public review):

Summary:

In this study, Farnsworth et al. ask whether the previously established expansion of mushroom bodies in the pollen foraging Heliconius genus of Heliconiini butterflies co-evolved with adaptations in the central complex. Heliconius trap line foraging strategies to acquire pollen as a novel resource require advanced spatial memory mediated by larger mushroom bodies, but the authors show that related navigation circuits in the central complex are highly conserved across the Heliconiini tribe, with a few interesting exceptions. Using general immunohistochemical stains and 3D reconstruction, the authors compared volumes of central complex regions, and unlike the mushroom bodies, there was no evidence of expansion associated with pollen feeding. However, a second dataset of neuromodulator and neuropeptide antibody labeling reveals more subtle differences between pollen and non-pollen foragers and highlights sub-circuits that may mediate species-specific differences in behavior. Specifically, the authors found an expansion of GABAergic ER neurons projecting to the fan-shaped body in Heliconius, which may enhance their ability to path-integrate. They also found differences in Allatostatin A immunoreactivity, particularly increased expression in the noduli associated with pollen feeding. These differences warrant closer examination in future studies to determine their functional implication on navigation and foraging behaviors.

Strengths:

The authors leveraged a large morphological data set from the Heliconiini to achieve excellent phylogenetic coverage across the tribe with 41 species represented. Their high-quality histology resolves anatomical details to the level of specific, identifiable tracts and cell body clusters. They revealed differences at a circuit level, which would not be obvious from a volumetric comparison. The discussion of these adaptations in the context of central complex models is useful for generating new hypotheses for future studies on the function of ER-FB neurons and the role of Allatostatin A modulation in navigation.

The conclusions drawn in this paper are measured and supported by rigorous statistics and evidence from micrographs.

Weaknesses:

The majority of results in this study do not reveal adaptations in the central complex associated with pollen foraging. However, reporting conserved traits is useful and illustrates where developmental or functional constraints may be acting. The implied hypothesis in the introduction is that expansion of mushroom bodies in Heliconius co-evolved with central complex adaptations, so it may be helpful to set up the alternate hypotheses in the beginning.

In the main text, the authors describe differences in GABAergic neurons "across several species" but only one Heliconius and one outgroup species seem to be represented in the figures. ER numbers in Figure 7H are only compared for these two species. If this data is available for other species, it would strengthen the paper to add them to the analysis, since this was one of the most intriguing findings in the study. I would want to know if the increased ER number is a trend in Heliconius or specific to H. melpomene.

Author response:

We thank the two reviewers for their constructive criticisms which we will address in the coming weeks, and we are confident doing so will benefit the manuscript.

We will aim to address all comments, but there are two main areas in particular that we highlight here:

(1)  Both reviewers make important suggestions to improve the readers’ understanding of the anatomical complexities and raw files we provide. We will generate annotated confocal stacks and simplify the nomenclature to better guide the reader through the more complex details of the anatomy of the central complex, and the neuron types we characterized more closely.

(2)  Both reviewers also pointed to several parts of our interpretations and discussion that should be clarified. We will do so by improving the language we use at certain sections to offer more precision, and by offering alternative explanations where possible.

Just thought I'd appreciate that the so-called "indispensable" and complex medicine sciences seem to rely on non-expert crowd information quite regularly!

Information can be shared and advanced through generations, but for animals the information is lost every time one dies.

All matter in the universe was just hydrogen at one point.

Bring all context together.

Humans are a tiny insignificant part of the larger universe.

Inductive reasoning is when something is decided through many observations

eLife Assessment

This study offers a valuable theoretical framework for quantifying molecular transport across interfaces between coexisting liquid phases, emphasizing interfacial resistance as a central factor governing transport kinetics. The mathematical derivations are solid. To enhance the paper's relevance and broaden its appeal, it would be helpful to clarify how the key equations connect to existing literature and to elucidate the physical mechanisms underlying scenarios that give rise to substantial interfacial resistance.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors theoretically address the topic of interface resistance between a phase-separated condensate and the surrounding dilute phase. In a nutshell, "interface resistance" occurs if material in the dilute phase can only slowly pass through the interface region to enter the dense phase. There is some evidence from FRAP experiments that such a resistance may exist, and if it does, it could be biologically relevant insofar as the movement of material between dense and dilute phases can be rate-limiting for biological processes, including coarsening. The current study theoretically addresses interface resistance at two levels of description: first, the authors present a simple way of formulating interface resistance for a sharp interface model. Second, they derive a formula for interface resistance for a finite-width interface and present two scenarios where the interface resistance might be substantial.

Strengths:

The topic is of broad relevance to the important field of intracellular phase separation, and the work is overall credible.

Weaknesses:

There are a few problems with the study as presented - mainly that the key formula for the latter section has already been derived and presented in Reference 6 (notably also in this journal), and that the physical basis for the proposed scenarios leading to a large interface resistance is not clearly supported.

(1) As noted, Equation 32 of the current study is entirely equivalent to Equation 8 of Reference 6, with a very similar derivation presented in Appendix 1 of that paper. In fact, Equation 8 in Reference 6 takes one more step by combining Equations 32 and 35 to provide a general expression for the interface resistance in an integral form. These prior results should be properly cited in the current work - the existing citations to Reference 6 do not make this overlap apparent.

(2) The authors of the current study go on to examine cases where this shared equation (here Equation 32) might imply a large interface resistance. The examples are mathematically correct, but physically unsupported. In order to produce a substantial interface resistance, the current authors have to suppose that in the interface region between the dense and dilute phases, either there is a local minimum of the diffusion coefficient or a local minimum of the density. I am not aware of any realistic model that would produce either of these minima. Indeed, the authors do not present sufficient examples or physical arguments that would support the existence of such minima.

In my view, these two issues limit the general interest of the latter portion of the current manuscript. While point 1 can be remedied by proper citation, point 2 is not so simple to address. The two ways the authors present to produce a substantial interface resistance seem to me to be mathematical exercises without a physical basis. The manuscript will improve if the authors can provide examples or compelling arguments for a minimum of either diffusion coefficient or density between the dense and dilute phases that would address point 2.

Reviewer #2 (Public review):

Summary:

This work provides a general theoretical framework for understanding molecular transport across liquid-liquid phase boundaries, focusing on interfacial resistance arising from deviations from local equilibrium. By bridging sharp and continuous interface descriptions, the authors demonstrate how distinct microscopic mechanisms can yield similar effective kinetics and propose practical experimental validation strategies.

Strengths:

(1) Conceptually rich and physically insightful interface resistance formulation in sharp and continuous limits.

(2) Strong integration of non-equilibrium thermodynamics with biologically motivated transport scenarios.

(3) Thorough numerical and analytical support, with thoughtful connection to current and emerging experimental techniques.

(4) Relevance to various systems, including biomolecular condensates and engineered aqueous two-phase systems.

Weaknesses:

(1) The work remains theoretical, mainly, with limited direct comparison to quantitative experimental data.

(2) The biological implications are only briefly explored; further discussion of specific systems where interface resistance might play a functional role would enhance the impact.

(3) Some model assumptions (e.g., symmetric labeling or idealized diffusivity profiles) could be further contextualized regarding biological variability.

Reviewer #3 (Public review):

The manuscript investigated the kinetics of molecule transport across interfaces in phase-separated mixtures. Through the development of a theoretical approach for a binary mixture in a sharp interface limit, the authors found that interface resistance leads to a slowdown in interfacial movement. Subsequently, they extended this approach to multiple molecular species (incorporating both labeled and unlabeled molecules) and continuous transport models. Finally, they proposed experimental settings in vitro and commented on the necessary optical resolution to detect signatures of interfacial kinetics associated with resistance.

The investigation of transport kinetics across biomolecular condensate interfaces holds significant relevance for understanding cellular function and dysfunction mechanisms; thus, the topic is important and timely. However, the current manuscript presentation requires improvement. Firstly, the inclusion of numerous equations in the main text substantially compromises readability, and relocation of a part of the formulae and derivations to the Appendix would be more appropriate. Secondly, the manuscript would benefit from more comprehensive comparisons with existing theoretical studies on molecular transport kinetics. The text should also be written to be more approachable for a general readership. Modifications and sufficient responses to the specific points outlined below are recommended.

(1) The authors introduced a theoretical framework to study the kinetics of molecules across an interface between two coexisting liquid phases and found that interface resistance leads to a slowdown in interfacial movement in a binary mixture and a decelerated molecule exchange between labeled and unlabeled molecules across the phase boundary. However, these findings appear rather expected. The work would be strengthened by a more thorough discussion of the kinetics of molecule transport across interfaces (such as the physical origin of the interface resistance and its specific impact on transport kinetics).

(2) The formulae in the manuscript should be checked and corrected. Notably, Equation 10 contains "\phi_2\ln\phi_2" while Eq. 11b shows "n^{-1}\ln\phi_2", suggesting a missing factor of "n^{-1}". Similarly, Equation 18 obtained from Equation 11: the logarithmic term in Eq.11a is "n^{-1}\ln phi_1-\ln(1-\phi)" but the pre-exponential factor in Equation 18a is just "\phi_1/(1-\phi*)", where is "n^{-1}"? Additionally, there is a unit inconsistency in Equation 36, where the unit of \rho (s/m) does not match that of the right-hand side expression (s/m^2).

(3) The authors stated that the numerical solutions are obtained using a custom finite difference scheme implemented in MATLAB in the Appendix. The description of numerical methods is insufficiently detailed and needs to be expanded, including specific equations or models used to obtain specific figures, the introduction of initial and boundary conditions, the choices of parameters and their reasons in terms of the biology.

(4) The authors claimed that their framework naturally extends to multiple molecular species, but only showed the situation of labeled and unlabeled molecules across a phase boundary. How about three or more molecular species? Does this framework still work? This should be added to strengthen the manuscript and confirm the framework's general applicability.

Author response:

Reviewer #1 (Public review): 

Summary: 

In this manuscript, the authors theoretically address the topic of interface resistance between a phase-separated condensate and the surrounding dilute phase. In a nutshell, "interface resistance" occurs if material in the dilute phase can only slowly pass through the interface region to enter the dense phase. There is some evidence from FRAP experiments that such a resistance may exist, and if it does, it could be biologically relevant insofar as the movement of material between dense and dilute phases can be rate-limiting for biological processes, including coarsening. The current study theoretically addresses interface resistance at two levels of description: first, the authors present a simple way of formulating interface resistance for a sharp interface model. Second, they derive a formula for interface resistance for a finite-width interface and present two scenarios where the interface resistance might be substantial. 

Strengths: 

The topic is of broad relevance to the important field of intracellular phase separation, and the work is overall credible. 

Weaknesses: 

There are a few problems with the study as presented - mainly that the key formula for the latter section has already been derived and presented in Reference 6 (notably also in this journal), and that the physical basis for the proposed scenarios leading to a large interface resistance is not clearly supported. 

(1) As noted, Equation 32 of the current study is entirely equivalent to Equation 8 of Reference 6, with a very similar derivation presented in Appendix 1 of that paper. In fact, Equation 8 in Reference 6 takes one more step by combining Equations 32 and 35 to provide a general expression for the interface resistance in an integral form. These prior results should be properly cited in the current work - the existing citations to Reference 6 do not make this overlap apparent. 

We agree and will make the overlap explicit, acknowledging priority and clarifying what is new here. The initial version of the preprint of Zhang et al. (2022) (https://www.biorxiv.org/content/10.1101/2022.03.16.484641v1) lacked the derivation (it referenced a Supplementary Note not yet available); it was added during the eLife submission. We worked from the preprint and missed this update, which we will now correct.

(2) The authors of the current study go on to examine cases where this shared equation (here Equation 32) might imply a large interface resistance. The examples are mathematically correct, but physically unsupported. In order to produce a substantial interface resistance, the current authors have to suppose that in the interface region between the dense and dilute phases, either there is a local minimum of the diffusion coefficient or a local minimum of the density. I am not aware of any realistic model that would produce either of these minima. Indeed, the authors do not present sufficient examples or physical arguments that would support the existence of such minima. 

We respectfully disagree with the reviewer on the physical plausibility of these scenarios there is both concrete experimental and theoretical evidence for the scenarios we discussed.

Experimental: Strom et al. (2017) (our reference 11) describes a substantially reduced protein diffusion coefficient at an in vivo phase boundary, while Hahn et al. (2011a) and Hahn et al. (2011b) (our references 27 and 28) describe transient accumulation of molecules at a phase boundary, which they attribute to the Donnan potential, but conceivably a lowered mobility could play a role.

Theoretical: Recent work (e.g., Majee et al. (2024)) shows that charged layers could form at phase boundaries, which could either repel or attract incoming molecules, depending on their charge, thus altering the local volume fraction, resulting in a trough or peak. Arguably, the model put forth by Zhang et al. (2024) could be mapped to a potential wall, where particles are reflected, unless in a certain state. We will add sentences to the corresponding results section, as well as the discussion to make this plausibility more apparent.

In my view, these two issues limit the general interest of the latter portion of the current manuscript. While point 1 can be remedied by proper citation, point 2 is not so simple to address. The two ways the authors present to produce a substantial interface resistance seem to me to be mathematical exercises without a physical basis. The manuscript will improve if the authors can provide examples or compelling arguments for a minimum of either diffusion coefficient or density between the dense and dilute phases that would address point 2. 

We believe we will be able to address both issues.

Reviewer #2 (Public review): 

Summary: 

This work provides a general theoretical framework for understanding molecular transport across liquid-liquid phase boundaries, focusing on interfacial resistance arising from deviations from local equilibrium. By bridging sharp and continuous interface descriptions, the authors demonstrate how distinct microscopic mechanisms can yield similar effective kinetics and propose practical experimental validation strategies. 

Strengths: 

(1) Conceptually rich and physically insightful interface resistance formulation in sharp and continuous limits. 

(2) Strong integration of non-equilibrium thermodynamics with biologically motivated transport scenarios. 

(3) Thorough numerical and analytical support, with thoughtful connection to current and emerging experimental techniques. 

(4) Relevance to various systems, including biomolecular condensates and engineered aqueous two-phase systems. 

Weaknesses: 

(1) The work remains theoretical, mainly, with limited direct comparison to quantitative experimental data. 

We agree with the reviewer, an experimental manuscript is in progress.

(2) The biological implications are only briefly explored; further discussion of specific systems where interface resistance might play a functional role would enhance the impact.

We thank the reviewer for this comment. We will add several such scenarios to the discussion, including the possibility to use interface resistance as a way of ordering biochemical reactions in time, as well as their potential to exclude molecules from condensates for long time periods, which, while not effective in the long-time limit, could help on cellular timescales of minutes to hours to respond to transient events.

(3) Some model assumptions (e.g., symmetric labeling or idealized diffusivity profiles) could be further contextualized regarding biological variability. 

The treatment of labelled and unlabelled molecules as physically identical is well supported by our experiments. Droplets under typical experimental conditions, i.e. when bleaching is not too strong, do not markedly change size or volume fraction of molecules, which would be expected if the physical properties like molecular volume or interaction strength were significantly changed. However, we do agree that in more extreme bleaching regimes the bleach step itself will change the droplet properties, but this can be avoided by tuning the FRAP laser power and dwell times accordingly.

Our diffusivity profiles are chosen in the simplest possible way to handle typical experimental constraints (large D outside, lower D inside, potentially lowered D at the boundary) and allow for a mean-field treatment. To the best of our knowledge, the precise make-up and concentration profiles of phase boundaries in biomolecular condensates are not currently known, due to limitations in optical resolution.

Reviewer #3 (Public review): 

The manuscript investigated the kinetics of molecule transport across interfaces in phase-separated mixtures. Through the development of a theoretical approach for a binary mixture in a sharp interface limit, the authors found that interface resistance leads to a slowdown in interfacial movement. Subsequently, they extended this approach to multiple molecular species (incorporating both labeled and unlabeled molecules) and continuous transport models. Finally, they proposed experimental settings in vitro and commented on the necessary optical resolution to detect signatures of interfacial kinetics associated with resistance. 

The investigation of transport kinetics across biomolecular condensate interfaces holds significant relevance for understanding cellular function and dysfunction mechanisms; thus, the topic is important and timely. However, the current manuscript presentation requires improvement. Firstly, the inclusion of numerous equations in the main text substantially compromises readability, and relocation of a part of the formulae and derivations to the Appendix would be more appropriate. Secondly, the manuscript would benefit from more comprehensive comparisons with existing theoretical studies on molecular transport kinetics. The text should also be written to be more approachable for a general readership. Modifications and sufficient responses to the specific points outlined below are recommended. 

(1) The authors introduced a theoretical framework to study the kinetics of molecules across an interface between two coexisting liquid phases and found that interface resistance leads to a slowdown in interfacial movement in a binary mixture and a decelerated molecule exchange between labeled and unlabeled molecules across the phase boundary. However, these findings appear rather expected. The work would be strengthened by a more thorough discussion of the kinetics of molecule transport across interfaces (such as the physical origin of the interface resistance and its specific impact on transport kinetics). 

We thank the reviewer for this comment and will discuss possible mechanisms and how they map to our meanfield model in more detail, both in the corresponding results section, and in the discussion, as also outlined in our response to Reviewer #1.

(2) The formulae in the manuscript should be checked and corrected. Notably, Equation 10 contains "\phi_2\ln\phi_2" while Eq. 11b shows "n^{-1}\ln\phi_2", suggesting a missing factor of "n^{-1}". Similarly, Equation 18 obtained from Equation 11: the logarithmic term in Eq.11a is "n^{^}{-1}\ln phi_1-\ln(1-\phi)" but the pre-exponential factor in Equation 18a is just "\phi_1/(1-\phi*)", where is "n^{^}{-1}"? Additionally, there is a unit inconsistency in Equation 36, where the unit of \rho (s/m) does not match that of the right-hand side expression (s/m^{^}2). 

We thank the reviewer. We identified that the error originates in the inline definition of the exchange chemical potential, already before equation 11. We inadvertently dropped a prefactor of n, which then shows up in the following equation as an exponent to (1-phi^{^}*). Very importantly this means the main result eq. 25 still holds, and in the revised manuscript we will correct the ensuing typographical mistakes.

(3) The authors stated that the numerical solutions are obtained using a custom finite difference scheme implemented in MATLAB in the Appendix. The description of numerical methods is insufficiently detailed and needs to be expanded, including specific equations or models used to obtain specific figures, the introduction of initial and boundary conditions, the choices of parameters and their reasons in terms of the biology.

We will substantially expand the Appendix for the numerical solutions and add an explanatory file to the repository to make clear how the code can be run, as well as its dependencies.

(4) The authors claimed that their framework naturally extends to multiple molecular species, but only showed the situation of labeled and unlabeled molecules across a phase boundary. How about three or more molecular species? Does this framework still work? This should be added to strengthen the manuscript and confirm the framework's general applicability. 

We have shown in Bo et al. (2021) that the labelling approach can be carried over to multi-component systems. Each species may, for example, encounter its own interface resistance. We will discuss this in more detail in the revised manuscript.

eLife Assessment

In this manuscript, the authors investigate the migration of human cortical interneurons under hypoxic conditions using forebrain assembloids and developing human brain tissue, and probe the underlying mechanisms. The study provides the first direct evidence that hypoxia delays interneuron migration and identifies adrenomedullin (ADM) as a potential therapeutic intervention. The findings are important, and the conclusions are convincingly supported by experimental evidence.

Reviewer #1 (Public review):

Summary:

This work aims to elucidate the molecular mechanisms affected in hypoxic conditions, causing reduced cortical interneuron migration. They use human assembloids as a migratory assay of subpallial interneurons into cortical organoids and show substantially reduced migration upon 24 hours of hypoxia. Bulk and scRNA-seq show adrenomedullin (ADM) up-regulation, as well as its receptor RAMP2, confirmed atthe protein level. Adding ADM to the culture medium after hypoxic conditions rescues the migration deficits, even though the subtype of interneurons affected is not examined. However, the authors demonstrate very clearly that ineffective ADM does not rescue the phenotype, and blocking RAMP2 also interferes with the rescue. The authors are also applauded for using 4 different cell lines and using human fetal cortex slices as an independent method to explore the DLXi1/2GFP-labelled iPSC-derived interneuron migration in this substrate with and without ADM addition (after confirming that also in this system ADM is up-regulated). Finally, the authors demonstrate PKA-CREB signalling mediating the effect of ADM addition, which also leads to up-regulation of GABAreceptors. Taken together, this is a very carefully done study on an important subject - how hypoxia affects cortical interneuron migration. In my view, the study is of great interest.

Strengths:

The strengths of the study are the novelty and the thorough work using several culture methods and 4 independent lines.

Weaknesses:

The main weakness is that other genes regulated upon hypoxia are not confirmed, such that readers will not know until which fold change/stats cut-off data are reliable.

Reviewer #2 (Public review):

Summary

The manuscript by Puno and colleagues investigates the impact of hypoxia on cortical interneuron migration and downstream signaling pathways. They establish two models to test hypoxia, cortical forebrain assembloids, and primary human fetal brain tissue. Both of these models provide a robust assay for interneuron migration. In addition, they find that ADM signaling mediates the migration deficits and rescue using exogenous ADM. The findings are novel and very interesting to the neurodevelopmental field, revealing new insights into how cortical interneurons migrate and as well, establishing exciting models for future studies. The authors use sufficient iPSC line,s including both XX and XY, so the analysis is robust. In addition, the RNAseq data with re-oxygenation is a nice control to see what genes are changed specifically due to hypoxia. Further, the overall level of validation of the sequencing data and involvement of ADM signaling is convincing, including the validation of ADM at the protein level. Overall, this is a very nice manuscript. I have a few comments and suggestions for the authors.

Strengths and Weaknesses:

(1) Can the authors comment on the possibility of inflammatory response pathways being activated by hypoxia? Has this been shown before? While not the focus of the manuscript, it could be discussed in the Discussion as an interesting finding and potential involvement of other cells in the Hypoxic response.

(2) Could the authors comment on the mechanism at play here with respect to ADM and binding to RAMP2 receptors - is this a potential autocrine loop, or is the source of ADM from other cell types besides inhibitory neurons? Given the scRNA-seq data, what cell-to-cell mechanisms can be at play? Since different cells express ADM, there could be different mechanisms in place in ventral vs dorsal areas.

(3) For data from Figure 6 - while the ELISA assays are informative to determine which pathways (PKA, AKT, ERK) are active, there is no positive control to indicate these assays are "working" - therefore, if possible, western blot analysis from assembloid tissue could be used (perhaps using the same lysates from Figure 3) as an alternative to validate changes at the protein level (however, this might prove difficult); further to this, is P-CREB activated at the protein level using WB?

(4) Could the authors comment further on the mechanism and what biological pathways and potential events are downstream of ADM binding to RAMP2 in inhibitory neurons? What functional impact would this have linked to the CREB pathway proposed? While the link to GABA receptors is proposed, CREB has many targets beyond this.

(5) Does hypoxia cause any changes to inhibitory neurogenesis (earlier stages than migration?) - this might always be known, but was not discussed.

(6) In the Discussion section, it might be worth detailing to the readers what the functional impact of delayed/reduced migration of inhibitory neurons into the cortex might result in, in terms of functional consequences for neural circuit development.

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether hypoxia disrupts the migration of human cortical interneurons, a process long suspected to underlie brain injury in preterm infants but previously inaccessible for direct study. Using human forebrain assembloids and ex vivo developing brain tissue, they visualized and quantified interneuron migration under hypoxic conditions, identified molecular components of the response, and explored the effect of pharmacological intervention (specifically ADM) on restoring the migration deficits.

Strengths:

The major strength of this study lies in its use of human forebrain assembloids and ex vivo prenatal brain tissue, which provide a direct system to study interneuron migration under hypoxic conditions. The authors combine multiple approaches: long-term live imaging to directly visualize interneuron migration, bulk and single-cell transcriptomics to identify hypoxia-induced molecular responses, pharmacological rescue experiments with ADM to establish therapeutic potential, and mechanistic assays implicating the cAMP/PKA/pCREB pathway and GABA receptor expression in mediating the effect. Together, this rigorous and multifaceted strategy convincingly demonstrates that hypoxia disrupts interneuron migration and that ADM can restore this defect through defined molecular mechanisms.

Overall, the authors achieve their stated aims, and the results strongly support their conclusions. The work has a significant impact by providing the first direct evidence of hypoxia-induced interneuron migration deficits in the human context, while also nominating a candidate therapeutic avenue. Beyond the specific findings, the methodological platform - particularly the combination of assembloids and live imaging - will be broadly useful to the community for probing neurodevelopmental processes in health and disease.

Weaknesses:

The main weakness of the study lies in the extent to which forebrain assembloids recapitulate in vivo conditions, as the migration of interneurons from hSO to hCO does not fully reflect the native environment or migratory context of these cells. Nevertheless, this limitation is tempered by the fact that the work provides the first direct observation of human interneuron migration under hypoxia, representing a major advance for the field. In addition, while the transcriptomic analyses are valuable and highlight promising candidates, more in-depth exploration will be needed to fully elucidate the molecular mechanisms governing neuronal migration and maturation under hypoxic conditions.

eLife Assessment

This work uses enhanced sampling molecular dynamics methods to generate potentially useful information about a conformational change (the DFG flip) that plays a key role in regulating kinase function and inhibitor binding. The focus of the work is on the mechanism of conformational change and how mutations affect the transition. The evidence supporting the conclusions is incomplete.

Reviewer #1 (Public review):

Summary:

The authors used weighted ensemble enhanced sampling molecular dynamics (MD) to test the hypothesis that a double mutant of Abl favors the DFG-in state relative to the WT and therefore causes the drug resistance to imatinib.

Strengths:

The authors employed the state-of-the-art weighted ensemble MD simulations with three novel progress coordinates to explore the conformational changes the DFG motif of Abl kinase. The hypothesis regarding the double mutant's drug resistance is novel.

Weaknesses:

The study contains many uncertain aspects. A major revision is needed to strengthen the support for the conclusions.

(1) Specifically, the authors need to define the DFG conformation using criteria accepted in the field, for example, see https://klifs.net/index.php.

(2) Convergence needs to be demonstrated for estimating the population difference between different conformational states.

(3) The DFG flip needs to be sampled several times to establish free energy difference.

(4) The free energy plots do not appear to show an intermediate state as claimed.

(5) The trajectory length of 7 ns in both Figure 2 and Figure 4 needs to be verified, as it is extremely short for a DFG flip that has a high free energy barrier.

(6) The free energy scale (100 kT) appears to be one order of magnitude too large.

(7) Setting the DFG-Asp to the protonated state is not justified, because in the DFG-in state, the DFG-Asp is clearly deprotonated.

(8) Finally, the authors should discuss their work in the context of the enormous progress made in theoretical studies and mechanistic understanding of the conformational landscape of protein kinases in the last two decades, particularly with regard to the DFG flip.

Reviewer #2 (Public review):

Summary:

This is a well-written manuscript on the mechanism of the DFG flip in kinases. This conformational change is important for the toggling of kinases between active (DFG-in) and inactive (DFG-out) states. The relative probabilities of these two states are also an important determinant of the affinity of inhibitors for a kinase. However, it is an extremely slow/rare conformational change, making it difficult to capture in simulations. The authors show that weighted ensemble simulations can capture the DFG flip and then delve into the mechanism of this conformational change and the effects of mutations.

Strengths:

The DFG flip is very hard to capture in simulations. Showing that this can be done with relatively little simulation by using enhanced sampling is a valuable contribution. The manuscript gives a nice description of the background for non-experts.

Weaknesses:

I was disappointed by the anecdotal approach to presenting the results. Molecular processes are stochastic and the authors have expertise in describing such processes. However, they chose to put most statistical analysis in the SI. The main text instead describes the order of events in single "representative" trajectories. The main text makes it sound like these were most selected as they were continuous trajectories from the weighted ensemble simulations. I would much rather hear a description of the highest probability pathway(s) with some quantification of how probable they are. That would give the reader a clear sense of how representative the events described are.

I appreciated the discussion of the strengths/weaknesses of weighted ensemble simulations. Am I correct that this method doesn't do anything to explicitly enhance sampling along orthogonal degrees of freedom? Maybe a point worth mentioning if so.

I don't understand Figure 3C. Could the authors instead show structures corresponding to each of the states in 3B, and maybe also a representative structure for pathways 1 and 2?

Why introduce S1 and DFG-inter? And why suppose that DFG-inter is what corresponds to the excited state seen by NMR?

It would be nice to have error bars on the populations reported in Figure 3.

I'm confused by the attempt to relate the relative probabilities of states to the 32 kca/mol barrier previously reported between the states. The barrier height should be related to the probability of a transition. The DFG-out state could be equiprobable with the DFG-in state and still have a 32 kcal/mol barrier separating them.

How do the relative probabilities of the DFG-in/out states compare to experiments, like NMR?

Do the staggered and concerted DFG flip pathways mentioned correspond to pathways 1 and 2 in Figure 3B, or is that a concept from previous literature?

eLife Assessment

This valuable work advances our understanding of the relation between multimodal MRI, cognition, and mental health. Convincing use of statistical learning techniques in UK Biobank data shows that 48% of the variance between an 11-task derived g-factor and imaging data can be explained. Overall, this paper contributes to the study of brain-behaviour relations and will be of interest for both its methods and its findings on how much variance in g can be explained.

[Editorial note: a previous version was reviewed by Biological Psychiatry]

Reviewer #1 (Public review):

Summary:

The authors aimed to examine how the covariation between cognition (represented by a g-factor based on 12 features of 11 cognitive tasks) and mental health (represented by 133 diverse features) is reflected in MR-based neural markers of cognition, as measured through multimodal neuroimaging (structural, rsfMRI, and diffusion MR). To integrate multiple neuroimaging phenotypes across MRI modalities, they used a so-called stacking approach, which employs two levels of machine learning. First, they built a predictive model from each neuroimaging phenotype to predict a target variable. Next, in the stacking level, they used predicted values (i.e., cognition predicted from each neuroimaging phenotype) from the first level as features to predict the target variable. To quantify the contribution of the neural indicators of cognition explaining the relationship between cognition and mental health, they conducted commonality analyses. Results showed that when they stacked neuroimaging phenotypes within dwMRI, rsMRI, and sMRI, they captured 25.5%, 29.8%, and 31.6% of the predictive relationship between cognition and mental health, respectively. By stacking all 72 neuroimaging phenotypes across three MRI modalities, they enhanced the explanation to 48%. Age and sex shared substantial overlapping variance with both mental health and neuroimaging in explaining cognition, accounting for 43% of the variance in the cognition-mental health relationship.

Strengths:

(1) A big study population (UK Biobank with 14000 subjects).

(2) The description of the methods (including Figure 1) is helpful in understanding the approach.

(3) This revised manuscript is much improved compared to the previous version.

Weaknesses:

(1) Although the background and reason for the study are better described in this version of the manuscript, the relevance of the question is, in my opinion, still questionable. The authors aimed to determine whether neural markers of cognition explain the covariance between cognition and mental health and which of the 72 MRI-based features contribute to explaining most of the covariance. I would like to invite the authors to make a stronger case for the relevance, keeping the clinical and scientific relevance in mind (what would you explain to the clinician, what would you explain to the people with lived experience, and how can this knowledge contribute to innovation in mental health care?).

(2) The discussion on the interpretation of the positive and negative PLRS loadings is not very convincing, and the findings are partly counterintuitive. For example (1) how to explain that distress has a positive loading and anxiety/trauma has a negative loading?; (2) how to explain that mental health features like wellbeing and happiness load in the same direction as psychosis and anxiety/trauma? From both a clinical and a neuroscientific perspective, this is hard to interpret.

(3) The analysis plan has not been preregistered (e.g. at OSF).

Note: the computational aspects of the methods fall beyond my expertise.

Reviewer #2 (Public review):

Summary:

The goal of this manuscript was to examine whether neural indicators explain the relationship between cognition and mental health. The authors achieved this aim by showing that the combination of MRI markers better predicted the cognition-mental health covariation.

Strengths:

The evidence supporting the conclusions is compelling. There is a large sample (UK biobank data) and a clear description of advanced analyses.

Weaknesses:

In the previous version of the paper, it was not completely clear what it means to look at the overlap between cognition and mental health. The authors have addressed this in the current version.

Author response:

Notes to Editors

We previously received comments from three reviewers at Biological Psychiatry, which we have addressed in detail below. The following is a summary of the reviewers’ comments along with our responses.

Reviewers 1 and 2 sought clearer justification for studying the cognition-mental health overlap (covariation) and its neuroimaging correlates. In the revised manuscripts, we expanded the Introduction and Discussion to explicitly outline the theoretical implications of investigating this overlap with machine learning. We also added nuance to the interpretation of the observed associations.

Reviewer 1 raised concerns about the accessibility of the machine learning methodology for readers without expertise in this field. We revised the Methods section to provide a clearer, step-by-step explanation of our machine learning approach, particularly the two-level machine learning through stacking. We also enhanced the description of the overall machine learning design, including model training, validation, and testing.

In response to Reviewer 2’s request for deeper interpretation of our findings and stronger theoretical grounding, we have expanded our discussion by incorporating a thorough interpretation of how mental health indices relate to cognition, material that was previously included only in supplementary materials due to word limit constraints. We have further strengthened the theoretical justification for our study design, with particular emphasis on the importance of examining shared variance between cognition and mental health through the derivation of neural markers of cognition. Additionally, to enhance the biological interpretation of our results, we included new analyses of feature importance across neuroimaging modalities, providing clearer insights into which neural features contribute most to the observed relationships.

Notably, Reviewer 3 acknowledged the strength of our study, including multimodal design, robust analytical approach, and clear visualization and interpretation of results. Their comments were exclusively methodological, underscoring the manuscript’s quality.

Reviewer 1:

The authors try to bridge mental health characteristics, global cognition and various MRI-derived (structural, diffusion and resting state fMRI) measures using the large dataset of UK Biobank. Each MRI modality alone explained max 25% of the cognitionmental health covariance, and when combined together 48% of the variance could be explained. As a peer-reviewer not familiar with the used methods (machine learning, although familiar with imaging), the manuscript is hard to read and I wonder what the message for the field might be. In the end of the discussion the authors state '... we provide potential targets for behavioural and physiological interventions that may affect cognition', the real relevance (and impact) of the findings is unclear to me.

Thank you for your thorough review and practical recommendations. We appreciate your constructive comments and suggestions and hope our revisions adequately address your concerns.

Major questions

(1) The methods are hard to follow for people not in this specific subfield, and therefore, I expect that for readers it is hard to understand how valid and how useful the approach is.

Thank you for your comment. To enhance accessibility for readers without a machine learning background, we revised the Methods section to clarify our analyses while retaining important technical details needed to understand our approach. Recognizing that some concepts may require prior knowledge, we provide detailed explanations of each analysis step, including the machine learning pipeline in the Supplementary Methods.

Line 188: “We employed nested cross-validation to predict cognition from mental health indices and 72 neuroimaging phenotypes (Fig. 1). Nested cross-validation is a robust method for evaluating machine-learning models while tuning their hyperparameters, ensuring that performance estimates are both accurate and unbiased. Here, we used a nested cross-validation scheme with five outer folds and ten inner folds.

We started by dividing the entire dataset into five outer folds. Each fold took a turn being held out as the outerfold test set (20% of the data), while the remaining four folds (80% of the data) were used as an outer-fold training set. Within each outer-fold training set, we performed a second layer of cross-validation – this time splitting the data into ten inner folds. These inner folds were used exclusively for hyperparameter tuning: models were trained on nine of the inner folds and validated on the remaining one, cycling through all ten combinations.

We then selected the hyperparameter configuration that performed best across the inner-fold validation sets, as determined by the minimal mean squared error (MSE). The model was then retrained on the full outer-fold training set using this hyperparameter configuration and evaluated on the outer-fold test set, using four performance metrics: Pearson r, the coefficient of determination ( R²), the mean absolute error (MAE), and the MSE. This entire process was repeated for each of the five outer folds, ensuring that every data point is used for both training and testing, but never at the same time. We opted for five outer folds instead of ten to reduce computational demands, particularly memory and processing time, given the substantial volume of neuroimaging data involved in model training. Five outer folds led to an outer-fold test set at least n = 4 000, which should be sufficient for model evaluation. In contrast, we retained ten inner folds to ensure robust and stable hyperparameter tuning, maximising the reliability of model selection.

To model the relationship between mental health and cognition, we employed Partial Least Squares Regression (PLSR) to predict the g-factor from 133 mental health variables. To model the relationship between neuroimaging data and cognition, we used a two-step stacking approach [15–17,61] to integrate information from 72 neuroimaging phenotypes across three MRI modalities. In the first step, we trained 72 base (first-level) PLSR models, each predicting the g-factor from a single neuroimaging phenotype. In the second step, we used the predicted values from these base models as input features for stacked models, which again predicted the g-factor. We constructed four stacked models based on the source of the base predictions: one each for dwMRI, rsMRI, sMRI, and a combined model incorporating all modalities (“dwMRI Stacked”, “rsMRI Stacked”, “sMRI Stacked”, and “All MRI Stacked”, respectively). Each stacked model was trained using one of four machine learning algorithms – ElasticNet, Random Forest, XGBoost, or Support Vector Regression – selected individually for each model (see Supplementary Materials, S6).

For rsMRI phenotypes, we treated the choice of functional connectivity quantification method – full correlation, partial correlation, or tangent space parametrization – as a hyperparameter. The method yielding the highest performance on the outer-fold training set was selected for predicting the g-factor (see Supplementary Materials, S5).

To prevent data leakage, we standardized the data using the mean and standard deviation derived from the training set and applied these parameters to the corresponding test set within each outer fold. This standardization was performed at three key stages: before g-factor derivation, before regressing out modality-specific confounds from the MRI data, and before stacking. Similarly, to maintain strict separation between training and testing data, both base and stacked models were trained exclusively on participants from the outer-fold training set and subsequently applied to the corresponding outer-fold test set.

To evaluate model performance and assess statistical significance, we aggregated the predicted and observed g_factor values from each outer-fold test set. We then computed a bootstrap distribution of Pearson’s correlation coefficient (_r) by resampling with replacement 5 000 times, generating 95% confidence intervals (CIs) (Fig. 1). Model performance was considered statistically significant if the 95% CI did not include zero, indicating that the observed associations were unlikely to have occurred by chance.”

(2) If only 40% of the cognition-mental health covariation can be explained by the MRI variables, how to explain the other 60% of the variance? And related to this %: why do the author think that 'this provides us confidence in using MRI to derive quantitative neuromarkers of cognition'?

Thank you for this insightful observation. Using the MRI modalities available in the UK Biobank, we were able to account for 48% of the covariation between cognition and mental health. The remaining 52% of unexplained variance may arise from several sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research from our group and others has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank.

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the Research Domain Criteria (RDoC) framework, brain circuits represent only one level of neurobiological analysis relevant to cognition. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. We have now incorporated these considerations into the Discussion section.

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Regarding our confidence in using MRI to derive neural markers for cognition, we base this on the predictive performance of MRI-based models. As we note in the Discussion (Line 554: “Consistent with previous studies, we show that MRI data predict individual differences in cognition with a medium-size performance (r ≈ 0.4) [15–17, 28, 61, 67, 68].”), the medium effect size we observed (r ≈ 0.4) agrees with existing literature on brain-cognition relationships, confirming that machine learning leads to replicable results. This effect size represents a moderate yet meaningful association in neuroimaging studies of aging, consistent with reports linking brain to behaviour in adults (Krämer et al., 2024; Tetereva et al., 2022). For example, a recent meta-analysis by Vieira and colleagues (2022) reported a similar effect size (r = 0.42, 95% CI [0.35;0.50]). Our study includes over 15000 participants, comparable to or more than typical meta-analyses, allowing us to characterise our work as a “mega-analysis”. And on top of this predictive performance, we found our neural markers for cognition to capture half of the cognition-mental health covariation, boosting our confidence in our approach.

Krämer C, Stumme J, da Costa Campos L, Dellani P, Rubbert C, Caspers J, et al. Prediction of cognitive performance differences in older age from multimodal neuroimaging data. GeroScience. 2024;46:283–308.

Tetereva A, Li J, Deng JD, Stringaris A, Pat N. Capturing brain cognition relationship: Integrating task‐based fMRI across tasks markedly boosts prediction and test‐retest reliability. NeuroImage. 2022;263:119588.

(3) Imagine that we can increase the explained variance using multimodal MRI measures, why is it useful? What does it learn us? What might be the implications?

We assume that by variance, Reviewer 1 referred to the cognition-mental health covariation mentioned in point 2) above.

If we can increase the explained cognition-mental health covariation using multimodal MRI measures, it would mean that we have developed a reasonable neuromarker that is close to RDoC’s neurobiological unit of analysis for cognition. RDoC treats cognition as one of the main basic functional domains that transdiagnostically underly mental health. According to RDoC, mental health should be studied in relation to cognition, alongside other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. RDoC further emphasizes that each domain, including cognition, should be investigated not only at the behavioural level but also through its neurobiological correlates. This means RDoC aims to discover neural markers of cognition that explain the covariation between cognition and mental health. For us, we approach the development of such neural markers using multimodal neuroimaging. We have now explained the motivation of our study in the first paragraph of the Introduction.

Line 43: “Cognition and mental health are closely intertwined [1]. Cognitive dysfunction is present in various mental illnesses, including anxiety [2, 3], depression [4–6], and psychotic disorders [7–12]. National Institute of Mental Health’s Research Domain Criteria (RDoC) [13,14] treats cognition as one of the main basic functional domains that transdiagnostically underly mental health. According to RDoC, mental health should be studied in relation to cognition, alongside other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. RDoC further emphasizes that each domain, including cognition, should be investigated not only at the behavioural level but also through its neurobiological correlates. In this study, we aim to examine how the covariation between cognition and mental health is reflected in neural markers of cognition, as measured through multimodal neuroimaging.”

More specific issues:

Introduction

(4) In the intro the sentence 'in some cases, altered cognitive functioning is directly related to psychiatric symptom severity' is in contrast to the next sentence '... are often stable and persist upon alleviation of psychiatric symptoms'.

Thank you for pointing this out. The first sentence refers to cases where cognitive deficits fluctuate with symptom severity, while the second emphasizes that core cognitive impairments often remain stable even during symptom remission. To avoid this confusion, we have removed these sentences.

(5) In the intro the text on the methods (various MRI modalities) is not needed for the Biol Psych readers audience.

We appreciate your comment. While some members of our target audience may have backgrounds in neuroimaging, machine learning, or psychiatry, we recognize that not all readers will be familiar with all three areas. To ensure accessibility for those who are not familiar with neuroimaging, we included a brief overview of the MRI modalities and quantification methods used in our study to provide context for the specific neuroimaging phenotypes. Additionally, we provided background information on the machine learning techniques employed, so that readers without a strong background in machine learning can still follow our methodology.

(6) Regarding age of the study sample: I understand that at recruitment the subjects' age ranges from 40 to 69 years. At MRI scanning the age ranges between about 46 to 82. How is that possible? And related to the age of the population: how did the authors deal with age in the analyses, since age is affecting both cognition as the brain measures?

Thank you for noticing this. In the Methods section, we first outline the characteristics of the UK Biobank cohort, including the age at first recruitment (40-69 years). Table 1 then shows the characteristics of participant subsamples included in each analysis. Since our study used data from Instance 2 (the second in-person visit), participants were approximately 5-13 years older at scanning, resulting in the age range of 46 to 82 years. We clarified the Table 1 caption as follows:

Line 113: “Table 1. Demographics for each subsample analysed: number, age, and sex of participants who completed all cognitive tests, mental health questionnaires, and MRI scanning”

We acknowledge that age may influence cognitive and neuroimaging measures. In our analyses, we intentionally preserved age-related variance in brain-cognition relationships across mid and late adulthood, as regressing out age completely would artificially remove biologically meaningful associations. At the same time, we rigorously addressed the effects of age and sex through additional commonality analyses quantifying age and sex contributions to the relationship between cognition and mental health.

As noted by Reviewer 1 and illustrated in Figure 8, age and sex shared substantial overlapping variance with both mental health and neuroimaging phenotypes in explaining cognitive outcomes. For example, in Figure 8i, age and sex together accounted for 43% of the variance in the cognition-mental health relationship:

(2.76 + 1.03) / (2.76 + 1.03 + 3.52 + 1.45) ≈ 0.43

Furthermore, neuromarkers from the all-MRI stacked model explained 72% of this age/sexrelated variance:

2.76 / (2.76 + 1.03) ≈ 0.72

This indicates that our neuromarkers captured a substantial portion of the cognition-mental health covariation that varied with age and sex, highlighting their relevance in age/sex-sensitive cognitive modeling.

In the Methods, Results, and Discussion, we say:

Methods

Line 263: “To understand how demographic factors, including age and sex, contribute to this relationship, we also conducted a separate set of commonality analyses treating age, sex, age2, age×sex, and age2×sex as an additional set of explanatory variables (Fig. 1).”

Results

Line 445: “Age and sex shared substantial overlapping variance with both mental health and neuroimaging in explaining cognition, accounting for 43% of the variance in the cognition-mental health relationship. Multimodal neural marker of cognition based on three MRI modalities (“All MRI Stacked”) explained 72% of this age and sex-related variance (Fig. 8i–l and Table S21).”

Discussion

Line 660: “We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.”

(7) Regarding the mental health variables: where characteristics with positive value (e.g. happiness and subjective wellbeing) reversely scored (compared to the negative items, such as anxiety, addition, etc)?

We appreciate you noting this. These composite scores primarily represent standard clinical measures such as the GAD-7 anxiety scale and N-12 neuroticism scale. We did not reverse the scores to keep their directionality, therefore making interpretability consistent with the original studies the scores were derived from (e.g., Davis et al., 2020; Dutt et al., 2022). Complete descriptive statistics for all mental health indices and detailed derivation procedures are provided in the Supplementary Materials (S2). On Page 6, Supplementary Methods, we say:

Line 92: “Composite mental health scores included the Generalized Anxiety Disorder (GAD-7), the Posttraumatic Stress Disorder (PTSD) Checklist (PCL-6), the Alcohol Use Disorders Identification Test (AUDIT), the Patient Health Questionnaire (PHQ-9) [12], the Eysenck Neuroticism (N-12), Probable Depression Status (PDS), and the Recent Depressive Symptoms (RDS-4) scores [13, 14]. To calculate the GAD-7, PCL-6, AUDIT, and PHQ-9, we used questions introduced at the online follow-up [12]. To obtain the N-12, PDS, and RDS-4 scores [14], we used data collected during the baseline assessment [13, 14].

We subcategorized depression and GAD based on frequency, current status (ever had depression or anxiety and current status of depression or anxiety), severity, and clinical diagnosis (depression or anxiety confirmed by a healthcare practitioner). Additionally, we differentiated between different depression statuses, such as recurrent depression, depression triggered by loss, etc. Variables related to self-harm were subdivided based on whether a person has ever self-harmed with the intent to die.

To make response scales more intuitive, we recorded responses within the well-being domain such that the lower score corresponded to a lesser extent of satisfaction (“Extremely unhappy”) and the higher score indicated a higher level of happiness (“Extremely happy”). For all questions, we assigned the median values to “Prefer not to answer” (-818 for in-person assessment and -3 for online questionnaire) and “Do not know” (-121 for in-person assessment and -1 for online questionnaire) responses. We excluded the “Work/job satisfaction” question from the mental health derivatives list because it included a “Not employed” response option, which could not be reasonably coded.

To calculate the risk of PTSD, we used questions from the PCL-6 questionnaire. Following Davis and colleagues [12], PCL-6 scores ranged from 6 to 29. A PCL-6 score of 12 or below corresponds to a low risk of meeting the Clinician-Administered PTSD Scale diagnostic criteria. PCL-6 scores between 13 and 16 and between 17 and 25 are indicative of an increased risk and high risk of PTSD, respectively. A score of above 26 is interpreted as a very high risk of PTSD [12, 15]. PTSD status was set to positive if the PCL-6 score exceeded or was equal to 14 and encompassed stressful events instead of catastrophic trauma alone [12].

To assess alcohol consumption, alcohol dependence, and harm associated with drinking, we calculated the sum of the ten questions from the AUDIT questionnaire [16]. We additionally subdivided the AUDIT score into the alcohol consumption score (questions 1-3, AUDIT-C) and the score reflecting problems caused by alcohol (questions 4-10, AUDIT-P) [17]. In questions 2-10 that followed the first trigger question (“Frequency of drinking alcohol”), we replaced missing values with 0 as they would correspond to a “Never” response to the first question.

An AUDIT score cut-off of 8 suggests moderate or low-risk alcohol consumption, and scores of 8 to 15 and above 15 indicate severe/harmful and hazardous (alcohol dependence or moderate-severe alcohol use disorder) drinking, respectively [16, 18]. Subsequently, hazardous alcohol use and alcohol dependence status correspond to AUDIT scores of ≥ 8 and ≥ 15, respectively. The “Alcohol dependence ever” status was set to positive if a participant had ever been physically dependent on alcohol. To reduce skewness, we logx+1-transformed the AUDIT, AUDIT-C, and AUDIT-P scores [17].”

Davis KAS, Coleman JRI, Adams M, Allen N, Breen G, Cullen B, et al. Mental health in UK Biobank – development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open. 2020;6:e18.

Dutt RK, Hannon K, Easley TO, Griffis JC, Zhang W, Bijsterbosch JD. Mental health in the UK Biobank: A roadmap to selfreport measures and neuroimaging correlates. Hum Brain Mapp. 2022;43:816–832.  

(8) In the discussion section (page 23, line 416-421), the authors refer to specific findings that are not described in the results section > I would add these findings to the main manuscript (including the discussion / interpretation).

We appreciate your careful reading. We agree that our original Results section did not explicitly describe the factor loadings for mental health in the PLSR model, despite discussing their implications later in the paper. We needed to include this part of the discussion in the Supplementary Materials to meet the word limit of the original submission. However, in response to your suggestion, we have now added the results regarding factor loadings to the Results section. We also moved the discussion of the association between mental health features and general cognition from the Supplementary Material to the manuscript’s Discussion.

Results

Line 298: “On average, information about mental health predicted the g-factor at  R²_mean = 0.10 and r_mean \= 0.31 (95% CI [0.291, 0.315]; Fig. 2b and 2c and Supplementary Materials, S9, Table S12). The magnitude and direction of factor loadings for mental health in the PLSR model allowed us to quantify the contribution of individual mental health indices to cognition. Overall, the scores for mental distress, alcohol and cannabis use, and self-harm behaviours relate positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events relate negatively to cognition.”

Discussion

Line 492: “Factor loadings derived from the PLSR model showed that the scores for mental distress, alcohol and cannabis use, and self-harm behaviours related positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events related negatively to the g-factor. Positive PLSR loadings of features related to mental distress may indicate greater susceptibility to or exaggerated perception of stressful events, psychological overexcitability, and predisposition to rumination in people with higher cognition [72]. On the other hand, these findings may be specific to the UK Biobank cohort and the way the questions for this mental health category were constructed. In particular, to evaluate mental distress, the UK Biobank questionnaire asked whether an individual sought or received medical help for or suffered from mental distress. In this regard, the estimate for mental distress may be more indicative of whether an individual experiencing mental distress had an opportunity or aspiration to visit a doctor and seek professional help [73]. Thus, people with better cognitive abilities and also with a higher socioeconomic status may indeed be more likely to seek professional help.

Limited evidence supports a positive association between self-harm behaviours and cognitive abilities, with some studies indicating higher cognitive performance as a risk factor for non-suicidal self-harm. Research shows an inverse relationship between cognitive control of emotion and suicidal behaviours that weakens over the life course [73,74]. Some studies have found a positive correlation between cognitive abilities and the risk of nonsuicidal self-harm, suicidal thoughts, and suicidal plans that may be independent of or, conversely, affected by socioeconomic status [75,76]. In our study, the magnitude of the association between self-harm behaviours and cognition was low (Fig. 2), indicating a weak relationship.

Positive PLSR loadings of features related to alcohol and cannabis may also indicate the influence of other factors. Overall, this relationship is believed to be largely affected by age, income, education, social status, social equality, social norms, and quality of life [79–80]. For example, education level and income correlate with cognitive ability and alcohol consumption [79,81–83]. Research also links a higher probability of having tried alcohol or recreational drugs, including cannabis, to a tendency of more intelligent individuals to approach evolutionary novel stimuli [84,85]. This hypothesis is supported by studies showing that cannabis users perform better on some cognitive tasks [86]. Alternatively, frequent drinking can indicate higher social engagement, which is positively associated with cognition [87]. Young adults often drink alcohol as a social ritual in university settings to build connections with peers [88]. In older adults, drinking may accompany friends or family visits [89,90]. Mixed evidence on the link between alcohol and drug use and cognition makes it difficult to draw definite conclusions, leaving an open question about the nature of this relationship.

Consistent with previous studies, we showed that anxiety and negative traumatic experiences were inversely associated with cognitive abilities [90–93]. Anxiety may be linked to poorer cognitive performance via reduced working memory capacity, increased focus on negative thoughts, and attentional bias to threatening stimuli that hinder the allocation of cognitive resources to a current task [94–96]. Individuals with PTSD consistently showed impaired verbal and working memory, visual attention, inhibitory function, task switching, cognitive flexibility, and cognitive control [97–100]. Exposure to traumatic events that did not reach the PTSD threshold was also linked to impaired cognition. For example, childhood trauma is associated with worse performance in processing speed, attention, and executive function tasks in adulthood, and age at a first traumatic event is predictive of the rate of executive function decline in midlife [101,102]. In the UK Biobank cohort, adverse life events have been linked to lower cognitive flexibility, partially via depression level [103].

In agreement with our findings, cognitive deficits are often found in psychotic disorders [104,105]. We treated neurological and mental health symptoms as predictor variables and did not stratify or exclude people based on psychiatric status or symptom severity. Since no prior studies have examined isolated psychotic symptoms (e.g., recent unusual experiences, hearing unreal voices, or seeing unreal visions), we avoid speculating on how these symptoms relate to cognition in our sample.

Finally, negative PLSR loadings of the features related to happiness and subjective well-being may be specific to the study cohort, as these findings do not agree with some previous research [107–109]. On the other hand, our results agree with the study linking excessive optimism or optimistic thinking to lower cognitive performance in memory, verbal fluency, fluid intelligence, and numerical reasoning tasks, and suggesting that pessimism or realism indicates better cognition [110]. The concept of realism/optimism as indicators of cognition is a plausible explanation for a negative association between the g-factor and friendship satisfaction, as well as a negative PLSR loading of feelings that life is meaningful, especially in older adults who tend to reflect more on the meaning of life [111]. The latter is supported by the study showing a negative association between cognitive function and the search for the meaning of life and a change in the pattern of this relationship after the age of 60 [112]. Finally, a UK Biobank study found a positive association of happiness with speed and visuospatial memory but a negative relationship with reasoning ability [113].”

(9) In the discussion section (page 24, line 440-449), the authors give an explanation on why the diffusion measure have limited utility, but the arguments put forward also concern structural and rsfMRI measures.

Thank you for this important observation. Indeed, the argument about voxel-averaged diffusion components (“… these metrics are less specific to the properties of individual white matter axons or bundles, and instead represent a composite of multiple diffusion components averaged within a voxel and across major fibre pathways”) could theoretically apply across other MRI modalities. We have therefore removed this point from the discussion to avoid overgeneralization. However, we maintain our central argument about the biological specificity of conventional tractography-derived diffusion metrics as their particular sensitivity to white matter microstructure (e.g., axonal integrity, myelin content) may make them better suited for detecting neuropathological changes than dynamic cognitive processes. This interpretation aligns with the mixed evidence linking these metrics to cognitive performance, despite their established utility in detecting white matter abnormalities in clinical populations (e.g., Bergamino et al., 2021; Silk et al., 2009). We clarify this distinction in the manuscript.

Line 572: “The somewhat limited utility of diffusion metrics derived specifically from probabilistic tractography in serving as robust quantitative neuromarkers of cognition and its shared variance with mental health may stem from their greater sensitivity and specificity to neuronal integrity and white matter microstructure rather than to dynamic cognitive processes. Critically, probabilistic tractography may be less effective at capturing relationships between white matter microstructure and behavioural scores cross-sectionally, as this method is more sensitive to pathological changes or dynamic microstructural alterations like those occurring during maturation. While these indices can capture abnormal white matter microstructure in clinical populations such as Alzheimer’s disease, schizophrenia, or attention deficit hyperactivity disorder (ADHD) [117–119], the empirical evidence on their associations with cognitive performance is controversial [114, 120–126].”

Bergamino M, Walsh RR, Stokes AM. Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer’s disease. Sci Rep. 2021;11:6990.

Silk TJ, Vance A, Rinehart N, Bradshaw JL, Cunnington R. White-matter abnormalities in attention deficit hyperactivity disorder: a diffusion tensor imaging study. Hum Brain Mapp. 2009;30:2757–2765.

Reviewer 2:

This is an interesting study combining a lot of data to investigate the link between cognition and mental health. The description of the study is very clear, it's easy to read for someone like me who does not have a lot of expertise in machine learning.

We thank you for your thorough review and constructive feedback. Your insightful comments have helped us identify conceptual and methodological aspects that required improvement in the manuscript. We have incorporated relevant changes throughout the paper, and below, we address each of your points in detail.

Comment 1: My main concern with this manuscript is that it is not yet clear to me what it exactly means to look at the overlap between cognition and mental health. This relation is r=0.3 which is not that high, so why is it then necessary to explain this overlap with neuroimaging measures? And, could it be that the relation between cognition and mental health is explained by third variables (environment? opportunities?). In the introduction I miss an explanation of why it is important to study this and what it will tell us, and in the discussion I would like to read some kind of 'answer' to these questions.

Thank you. It’s important to clarify why we investigated the relationship between cognition and mental health, and what we found using data from the UK Biobank.

Conceptually, our work is grounded in the Research Domain Criteria (RDoC; Insel et al., 2010) framework. RDoC conceptualizes mental health not through traditional diagnostic categories, but through core functional domains that span the full spectrum from normal to abnormal functioning. These domains include cognition, negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. Within this framework, cognition is considered a fundamental domain that contributes to mental health across diagnostic boundaries. Meta-analytic evidence supports a link between cognitive functioning and mental health (Abramovitch, et al., 2021; East-Richard, et al., 2020). In the context of a large, population-based dataset like the UK Biobank, this implies that cognitive performance – as measured by various cognitive tasks – should be meaningfully associated with available mental health indicators.

However, because cognition is only one of several functional domains implicated in mental health, we do not expect the covariation between cognition and mental health to be very high. Other domains, such as negative and positive valence systems, arousal and regulatory systems, or social processing, may also play significant roles. Theoretically, this places an upper bound on the strength of the cognition-mental health relationship, especially in normative, nonclinical samples.

Our current findings from the UK Biobank reflect this. Most of the 133 mental health variables showed relatively weak individual correlations with cognition (mean r \= 0.01, SD = 0.05, min r \= –0.08, max r \= 0.17; see Figure 2). However, using a PLS-based machine learning approach, we were able to integrate information across all mental-health variables to predict cognition, yielding an out-of-sample correlation of r = 0.31 [95% CI: 0.29, 0.32].  

We believe this estimate approximates the true strength of the cognition-mental health relationship in normative samples, consistent with both theoretical expectations and prior empirical findings. Theoretically, this aligns with the RDoC view that cognition is one of several contributing domains. Empirically, our results are consistent with findings from our previous mega-analysis in children (Wang et al., 2025). Moreover, in the field of gerontology, an effect size of r = 0.31 is not considered small. According to Brydges (2019), it falls around the 70th percentile of effect sizes reported in gerontological studies and approaches the threshold for a large effect (r \= 0.32). Given that most studies report within-sample associations, our out-of-sample results are likely more robust and generalizable (Yarkoni & Westfall, 2017).

To answer, “why is it then necessary to explain this overlap with neuroimaging measures”, we again draw on the conceptual foundation of the RDoC framework. RDoC emphasizes that each functional domain, such as cognition, should be studied not only at the behavioural level but also across multiple neurobiological units of analysis, including genes, molecules, cells, circuits, physiology, and behaviour.

MRI-based neural markers represent one such level of analysis. While other biological systems (e.g., genetic, molecular, or physiological) also contribute to the cognition-mental health relationship, neuroimaging provides unique insights into the brain mechanisms underlying this association – insights that cannot be obtained from behavioural data alone.

In response to the related question, “Could the relationship between cognition and mental health be explained by third variables (e.g., environment, opportunities)?”, we note that developing a neural marker of cognition capable of capturing its relationship with mental health is the central aim of this study. Using the MRI modalities available in the UK Biobank, we were able to account for 48% of the covariation between cognition and mental health.

The remaining 52% of unexplained variance may stem from several sources. According to the RDoC framework, neuromarkers could be further refined by incorporating additional neuroimaging modalities (e.g., task-based fMRI, PET, ASL, MEG/EEG, fNIRS) and integrating other units of analysis such as genetic, molecular, cellular, and physiological data.

Once more comprehensive neuromarkers are developed, capturing a greater proportion of the cognition-mental health covariation, they may also lead to new research direction – to investigate how environmental factors and life opportunities influence these markers. However, exploring those environmental contributions lies beyond the scope of the current study.

We discuss these considerations and explain the motivation of our study in the revised Introduction and Discussion.

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Introduction

Line 43: “Cognition and mental health are closely intertwined [1]. Cognitive dysfunction is present in various mental illnesses, including anxiety [2, 3], depression [4–6], and psychotic disorders [7–12]. National Institute of Mental Health’s Research Domain Criteria (RDoC) [13,14] treats cognition as one of the main basic functional domains that transdiagnostically underly mental health. According to RDoC, mental health should be studied in relation to cognition, alongside other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. RDoC further emphasizes that each domain, including cognition, should be investigated not only at the behavioural level but also through its neurobiological correlates. In this study, we aim to examine how the covariation between cognition and mental health is reflected in neural markers of cognition, as measured through multimodal neuroimaging.”

Discussion

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. AJP. 2010;167:748–751.

Abramovitch, A., Short, T., & Schweiger, A. (2021). The C Factor: Cognitive dysfunction as a transdiagnostic dimension in psychopathology. Clinical Psychology Review, 86, 102007.

East-Richard, C., R. -Mercier, A., Nadeau, D., & Cellard, C. (2020). Transdiagnostic neurocognitive deficits in psychiatry: A review of meta-analyses. Canadian Psychology / Psychologie Canadienne, 61(3), 190–214.

Wang Y, Anney R, Pat N. The relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis. eLife. 2025.14:RP105537.

Brydges CR. Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology. Innovation in Aging. 2019;3(4):igz036.

Yarkoni T, Westfall J. Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspect Psychol Sci. 2017;12(6):1100-1122.

Comment 2 Title: - Shouldn't it be "MRI markers" (plural)?

We used the singular form (“marker”) intentionally, as it refers to the composite neuroimaging marker derived from all three MRI modalities in our stacked model. This multimodal marker represents the combined predictive power of all modalities and captures the highest proportion of the mental health-cognition relationship in our analyses.

Comment 3: Introduction - I miss an explanation of why it is useful to look at cognition-mental health covariation

We believe we have sufficiently addressed this comment in our response to Reviewer 2, comment 1 above.

Comment 4: - "Demonstrating that MRI-based neural indicators of cognition capture the covariation between cognition and mental health will thereby support the utility of such indicators for understanding the etiology of mental health" (page 4, line 56-58) - how/why?

Previous research has largely focused on developing MRI-based neural indicators that accurately predict cognitive performance (Marek et al., 2022; Vieira et al., 2020). Building on this foundation, our findings further demonstrate that the predictive performance of a neural indicator for cognition is closely tied to its ability to explain the covariation between cognition and mental health. In other words, the robustness of a neural indicator – its capacity to capture individual differences in cognition – is strongly associated with how well it reflects the shared variance between cognition and mental health.

This insight is particularly important within the context of the RDoC framework, which seeks to understand the etiology of mental health through functional domains (such as cognition) and their underlying neurobiological units of analysis (Insel et al., 2010). According to RDoC, for a neural indicator of cognition to be informative for mental health research, it must not only predict cognitive performance but also capture its relationship with mental health.

Furthermore, RDoC emphasizes the integration of neurobiological measures to investigate the influence of environmental and developmental factors on mental health. In line with this, our neural indicators of cognition may serve as valuable tools in future research aimed at understanding how environmental exposures and developmental trajectories shape mental health outcomes. We discuss this in more detail in the revised Discussion.

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, et al. Reproducible brain-wide association studies require thousands of individuals. Nature. 2022;603:654–660.

Vieira S, Gong QY, Pinaya WHL, et al. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull. 2020;46(1):17-26.

Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. AJP. 2010;167:748–751.

Comment 5: - The explanation about the stacking approach is not yet completely clear to me. I don't understand how the target variable can be the dependent variable in both step one and two. Or are those different variables? It would be helpful to also give an example of the target variable in line 88 on page 5

Thank you for this excellent question. In our stacking approach, the same target variable, the g-factor, is indeed used across both modeling stages, but with a key distinction in how predictions are generated and integrated.

In the first-level models, we trained separate Partial Least Squares Regression (PLSR) models for each of the 72 neuroimaging phenotypes, each predicting the g-factor independently. The predicted values from these 72 models were then used as input features for the second-level stacked model, which combined them to generate a final prediction of the g-factor. This twostage framework enables us to integrate information across multiple imaging modalities while maintaining a consistent prediction target.

To avoid data leakage, both modeling stages were conducted entirely within the training set for each cross-validation fold. Only after the second-level model was trained was it applied to the outer-fold test participants who were not involved in any part of the model training process.

To improve accessibility, we have revised the Methods section (see Page 10) to clarify this approach, ensuring that the description remains technically accurate while being easier to follow.

Line 188: “We employed nested cross-validation to predict cognition from mental health indices and 72 neuroimaging phenotypes (Fig. 1). Nested cross-validation is a robust method for evaluating machine-learning models while tuning their hyperparameters, ensuring that performance estimates are both accurate and unbiased. Here, we used a nested cross-validation scheme with five outer folds and ten inner folds.

We started by dividing the entire dataset into five outer folds. Each fold took a turn being held out as the outerfold test set (20% of the data), while the remaining four folds (80% of the data) were used as an outer-fold training set. Within each outer-fold training set, we performed a second layer of cross-validation – this time splitting the data into ten inner folds. These inner folds were used exclusively for hyperparameter tuning: models were trained on nine of the inner folds and validated on the remaining one, cycling through all ten combinations.

We then selected the hyperparameter configuration that performed best across the inner-fold validation sets, as determined by the minimal mean squared error (MSE). The model was then retrained on the full outer-fold training set using this hyperparameter configuration and evaluated on the outer-fold test set, using four performance metrics: Pearson r, the coefficient of determination ( R²), the mean absolute error (MAE), and the MSE. This entire process was repeated for each of the five outer folds, ensuring that every data point is used for both training and testing, but never at the same time. We opted for five outer folds instead of ten to reduce computational demands, particularly memory and processing time, given the substantial volume of neuroimaging data involved in model training. Five outer folds led to an outer-fold test set at least n = 4 000, which should be sufficient for model evaluation. In contrast, we retained ten inner folds to ensure robust and stable hyperparameter tuning, maximising the reliability of model selection.

To model the relationship between mental health and cognition, we employed Partial Least Squares Regression (PLSR) to predict the g-factor from 133 mental health variables. To model the relationship between neuroimaging data and cognition, we used a two-step stacking approach [15–17,61] to integrate information from 72 neuroimaging phenotypes across three MRI modalities. In the first step, we trained 72 base (first-level) PLSR models, each predicting the g-factor from a single neuroimaging phenotype. In the second step, we used the predicted values from these base models as input features for stacked models, which again predicted the g-factor. We constructed four stacked models based on the source of the base predictions: one each for dwMRI, rsMRI, sMRI, and a combined model incorporating all modalities (“dwMRI Stacked”, “rsMRI Stacked”, “sMRI Stacked”, and “All MRI Stacked”, respectively). Each stacked model was trained using one of four machine learning algorithms – ElasticNet, Random Forest, XGBoost, or Support Vector Regression – selected individually for each model (see Supplementary Materials, S6).

For rsMRI phenotypes, we treated the choice of functional connectivity quantification method – full correlation, partial correlation, or tangent space parametrization – as a hyperparameter. The method yielding the highest performance on the outer-fold training set was selected for predicting the g-factor (see Supplementary Materials, S5).

To prevent data leakage, we standardized the data using the mean and standard deviation derived from the training set and applied these parameters to the corresponding test set within each outer fold. This standardization was performed at three key stages: before g-factor derivation, before regressing out modality-specific confounds from the MRI data, and before stacking. Similarly, to maintain strict separation between training and testing data, both base and stacked models were trained exclusively on participants from the outer-fold training set and subsequently applied to the corresponding outer-fold test set.

To evaluate model performance and assess statistical significance, we aggregated the predicted and observed gfactor values from each outer-fold test set. We then computed a bootstrap distribution of Pearson’s correlation coefficient (r) by resampling with replacement 5 000 times, generating 95% confidence intervals (CIs) (Fig. 1). Model performance was considered statistically significant if the 95% CI did not include zero, indicating that the observed associations were unlikely to have occurred by chance.”

Comment 6: Methods - It's not clear from the text and Figure 1 which 12 scores from 11 tests are being used to derive the g-factor. Figure 1 shows only 8 bullet points with 10 scores in A and 13 tests under 'Cognitive tests' in B. Moreover, Supplement S1 describes 12 tests and 14 measures (Prospective Memory test is in the text but not in Supplementary Table 1).

Thank you for identifying this discrepancy. In the original Figure 1b and in the Supplementary Methods (S1), the “Prospective Memory” test was accidentally duplicated, while it was present in the Supplementary Table 1 (Line 53, Supplementary Table 1). We have now corrected both figures for consistency. To clarify: Figure 1a presents the global mental health and cognitive domains studied, while Figure 1b now accurately lists 1) the 12 cognitive scores from 11 tests used to derive the g-factor (with the Trail Making Test contributing two measures – numeric and alphabetic trails) and 2) the three main categories of mental health indices used as machine learning features.

We also corrected the Supplementary Materials to remove the duplicate test from the first paragraph. In Supplementary Table 1, there were 11 tests listed, and for the Trail Making test, we specified in the “Core measures” column that this test had 2 derivative scores: duration to complete the numeric path (Trail 1) and duration to complete the alphabetic path (Trail 2).

Supplementary Materials, Line 46: “We used twelve scores from the eleven cognitive tests that represented the following cognitive domains: reaction time and processing speed (Reaction Time test), working memory (Numeric Memory test), verbal and numerical reasoning (Fluid Intelligence test), executive function (Trail Making Test), non-verbal fluid reasoning (Matrix Pattern Completion test), processing speed (Symbol Digit Substitution test), vocabulary (Picture Vocabulary test), planning abilities (Tower Rearranging test), verbal declarative memory (Paired Associate Learning test), prospective memory (Prospective Memory test), and visual memory (Pairs Matching test) [1].”

Comment 7: - For the mental health measures: If I understand correctly, the questionnaire items were used individually, but also to create composite scores. This seems counterintuitive, because I would assume that if the raw data is used, the composite scores would not add additional information to that. When reading the Supplement, it seems like I'm not correct… It would be helpful to clarify the text on page 7 in the main text.

You raise an excellent observation regarding the use of both individual questionnaire items and composite scores. This dual approach was methodologically justified by the properties of Partial Least Squares Regression (PLSR), our chosen first-level machine learning algorithm, which benefits from rich feature sets and can handle multicollinearity through dimensionality reduction. PLSR transforms correlated features into latent variables, meaning both individual items and composite scores can contribute unique information to the model. We elaborate on PLSR's mathematical principles in Supplementary Materials (S5).

To directly address this concern, we conducted comparative analyses showing that the PLSR model (a single 80/20% training/test split), incorporating all 133 mental health features (both items and composites), outperformed models using either type alone. The full model achieved superior performance (MSE = 0.458, MAE = 0.537, R² \= 0.112, Pearson r = 0.336, p-value = 6.936e-112) compared to using only composite scores (93 features; MSE = 0.461, MAE = 0.538, R² = 0.107, Pearson r = 0.328, p-value = 5.8e-106) or only questionnaire items (40 features; MSE = 0.499, MAE = 0.561, R² = 0.033, Pearson r = 0.184, p-value = 2.53e-33). These results confirm that including both data types provide complementary predictive value. We expand on these considerations in the revised Methods section.

Line 123: “Mental health measures encompassed 133 variables from twelve groups: mental distress, depression, clinical diagnoses related to the nervous system and mental health, mania (including bipolar disorder), neuroticism, anxiety, addictions, alcohol and cannabis use, unusual/psychotic experiences, traumatic events, selfharm behaviours, and happiness and subjective well-being (Fig. 1 and Tables S4 and S5). We included both selfreport questionnaire items from all participants and composite diagnostic scores computed following Davis et al. and Dutt et al. [35,36] as features in our first-level (for explanation, see Data analysis section) Partial Least Squares Regression (PLSR) model. This approach leverages PLSR’s ability to handle multicollinearity through dimensionality reduction, enabling simultaneous use of granular symptom-level information and robust composite measures (for mental health scoring details, see Supplementary Materials, S2). We assess the contribution of each mental health index to general cognition by examining the direction and magnitude of its PLSR-derived loadings on the identified latent variables”

Comment 8: - Results - The colors in Figure 4 B are a bit hard to differentiate.

We have updated Figure 4 to enhance colour differentiation by adjusting saturation and brightness levels, improving visual distinction. For further clarity, we split the original figure into two separate figures.

Comment 9: - Discussion - "Overall, the scores for mental distress, alcohol and cannabis use, and self-harm behaviours relate positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events relate negatively to cognition," - this seems counterintuitive, that some symptoms relate to better cognition and others relate to worse cognition. Could you elaborate on this finding and what it could mean?

We appreciate you highlighting this important observation. While some associations between mental health indices and cognition may appear counterintuitive at first glance, these patterns are robust (emerging consistently across both univariate correlations and PLSR loadings) and align with previous literature (e.g., Karpinski et al., 2018; Ogueji et al., 2022). For instance, the positive relationship between cognitive ability and certain mental health indicators like help-seeking behaviour has been documented in other population studies (Karpinski et al., 2018; Ogueji et al., 2022), potentially reflecting greater health literacy and access to care among cognitively advantaged individuals. Conversely, the negative associations with conditions like psychotic experiences mirror established neurocognitive deficits in these domains.

As was initially detailed in Supplementary Materials (S12) and now expanded in our Discussion, these findings likely reflect complex multidimensional interactions. The positive loadings for mental distress indicators may capture: (1) greater help-seeking behaviour among those with higher cognition and socioeconomic resources, and/or (2) psychological overexcitability and rumination tendencies in high-functioning individuals. These interpretations are particularly relevant to the UK Biobank's assessment methods, where mental distress items focused on medical help-seeking rather than symptom severity per se (e.g., as a measure of mental distress, the UK Biobank questionnaire asked whether an individual sought or received medical help for or suffered from mental distress).

Line 492: “Factor loadings derived from the PLSR model showed that the scores for mental distress, alcohol and cannabis use, and self-harm behaviours related positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events related negatively to the g-factor. Positive PLSR loadings of features related to mental distress may indicate greater susceptibility to or exaggerated perception of stressful events, psychological overexcitability, and predisposition to rumination in people with higher cognition [72]. On the other hand, these findings may be specific to the UK Biobank cohort and the way the questions for this mental health category were constructed. In particular, to evaluate mental distress, the UK Biobank questionnaire asked whether an individual sought or received medical help for or suffered from mental distress. In this regard, the estimate for mental distress may be more indicative of whether an individual experiencing mental distress had an opportunity or aspiration to visit a doctor and seek professional help [73]. Thus, people with better cognitive abilities and also with a higher socioeconomic status may indeed be more likely to seek professional help.

Limited evidence supports a positive association between self-harm behaviours and cognitive abilities, with some studies indicating higher cognitive performance as a risk factor for non-suicidal self-harm. Research shows an inverse relationship between cognitive control of emotion and suicidal behaviours that weakens over the life course [73,74]. Some studies have found a positive correlation between cognitive abilities and the risk of nonsuicidal self-harm, suicidal thoughts, and suicidal plans that may be independent of or, conversely, affected by socioeconomic status [75,76]. In our study, the magnitude of the association between self-harm behaviours and cognition was low (Fig. 2), indicating a weak relationship.

Positive PLSR loadings of features related to alcohol and cannabis may also indicate the influence of other factors. Overall, this relationship is believed to be largely affected by age, income, education, social status, social equality, social norms, and quality of life [79–80]. For example, education level and income correlate with cognitive ability and alcohol consumption [79,81–83]. Research also links a higher probability of having tried alcohol or recreational drugs, including cannabis, to a tendency of more intelligent individuals to approach evolutionary novel stimuli [84,85]. This hypothesis is supported by studies showing that cannabis users perform better on some cognitive tasks [86]. Alternatively, frequent drinking can indicate higher social engagement, which is positively associated with cognition [87]. Young adults often drink alcohol as a social ritual in university settings to build connections with peers [88]. In older adults, drinking may accompany friends or family visits [89,90]. Mixed evidence on the link between alcohol and drug use and cognition makes it difficult to draw definite conclusions, leaving an open question about the nature of this relationship.

Consistent with previous studies, we showed that anxiety and negative traumatic experiences were inversely associated with cognitive abilities [90–93]. Anxiety may be linked to poorer cognitive performance via reduced working memory capacity, increased focus on negative thoughts, and attentional bias to threatening stimuli that hinder the allocation of cognitive resources to a current task [94–96]. Individuals with PTSD consistently showed impaired verbal and working memory, visual attention, inhibitory function, task switching, cognitive flexibility, and cognitive control [97–100]. Exposure to traumatic events that did not reach the PTSD threshold was also linked to impaired cognition. For example, childhood trauma is associated with worse performance in processing speed, attention, and executive function tasks in adulthood, and age at a first traumatic event is predictive of the rate of executive function decline in midlife [101,102]. In the UK Biobank cohort, adverse life events have been linked to lower cognitive flexibility, partially via depression level [103].

In agreement with our findings, cognitive deficits are often found in psychotic disorders [104,105]. We treated neurological and mental health symptoms as predictor variables and did not stratify or exclude people based on psychiatric status or symptom severity. Since no prior studies have examined isolated psychotic symptoms (e.g., recent unusual experiences, hearing unreal voices, or seeing unreal visions), we avoid speculating on how these symptoms relate to cognition in our sample.

Finally, negative PLSR loadings of the features related to happiness and subjective well-being may be specific to the study cohort, as these findings do not agree with some previous research [107–109]. On the other hand, our results agree with the study linking excessive optimism or optimistic thinking to lower cognitive performance in memory, verbal fluency, fluid intelligence, and numerical reasoning tasks, and suggesting that pessimism or realism indicates better cognition [110]. The concept of realism/optimism as indicators of cognition is a plausible explanation for a negative association between the g-factor and friendship satisfaction, as well as a negative PLSR loading of feelings that life is meaningful, especially in older adults who tend to reflect more on the meaning of life [111]. The latter is supported by the study showing a negative association between cognitive function and the search for the meaning of life and a change in the pattern of this relationship after the age of 60 [112]. Finally, a UK Biobank study found a positive association of happiness with speed and visuospatial memory but a negative relationship with reasoning ability [113].”

Karpinski RI, Kinase Kolb AM, Tetreault NA, Borowski TB. High intelligence: A risk factor for psychological and physiological overexcitabilities. Intelligence. 2018;66:8–23.

Ogueji IA, Okoloba MM. Seeking Professional Help for Mental Illness: A Mixed-Methods Study of Black Family Members in the UK and Nigeria. Psychol Stud. 2022;67:164–177.

Comment 10: - All neuroimaging factors together explain 48% of the variance in the cognition-mental health relationship. However, this relationship is only r=0.3 - so then the effect of neuroimaging factors seems a lot smaller… What does it mean?

Thank you for raising this critical point. We have addressed this point in our response to Reviewer 1, comment 2, Reviewer 1, comment 3 and Reviewer 2, comment 1.

Briefly, cognition is related to mental health at around r = 0.3 and to neuroimaging phenotypes at around r = 0.4. These levels of relationship strength are consistent to what has been shown in the literature (e.g., Wang et al., 2025 and Vieira et al., 2020). We discussed the relationship between cognition and mental health in our response to Reviewer 2, comment 1 above. In short, this relationship reflects just one functional domain – mental health may also be associated with other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. Moreover, in the context of gerontology research, this effect size is considered relatively large (Brydges et al., 2019).

We conducted a commonality analysis to investigate the unique and shared variance of mental health and neuroimaging phenotypes in explaining cognition.  As we discussed in our response to Reviewer 1, comment 2, we were able to account for 48% of the covariation between cognition and mental health using the MRI modalities available in the UK Biobank. The remaining 52% of unexplained variance may arise from several sources.

One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research from our group and others has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank (Tetereva et al., 2025).

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

We have now incorporated these considerations into the Discussion section.

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Wang Y, Anney R, Pat N. The relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis. eLife. 2025.14:RP105537.

Vieira S, Gong QY, Pinaya WHL, et al. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull. 2020;46(1):17-26.

Brydges CR. Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology. Innovation in Aging. 2019;3(4):igz036.

Tetereva A, Knodt AR, Melzer TR, et al. Improving Predictability, Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking. Preprint. bioRxiv. 2025;2024.05.03.589404.

Reviewer 3:

Buianova et al. present a comprehensive analysis examining the predictive value of multimodal neuroimaging data for general cognitive ability, operationalized as a derived g-factor. The study demonstrates that functional MRI holds the strongest predictive power among the modalities, while integrating multiple MRI modalities through stacking further enhances prediction performance. The inclusion of a commonality analysis provides valuable insight into the extent to which shared and unique variance across mental health features and neuroimaging modalities contributes to the observed associations with cognition. The results are clearly presented and supported by highquality visualizations. Limitations of the sample are stated clearly.

Thank you once more for your constructive and encouraging feedback. We appreciate your careful reading and valuable methodological insights. Your expertise has helped us clarify key methodological concepts and improve the overall rigour of our study.

Suggestions for improvement:

(1) The manuscript would benefit from the inclusion of permutation testing to evaluate the statistical significance of the predictive models. This is particularly important given that some of the reported performance metrics are relatively modest, and permutation testing could help ensure that results are not driven by chance.

Thank you, this is an excellent point. We agree that evaluating the statistical significance of our predictive models is essential.

In our original analysis, we assessed model performance by generating a bootstrap distribution of Pearson’s r, resampling the data with replacement 5,000 times (see Figure 3b). In response to your feedback, we have made the following updates:

(1) Improved Figure 3b to explicitly display the 95% confidence intervals.

(2) Supplemented the results by reporting the exact confidence interval values.

(3) Clarified our significance testing procedure in the Methods section.

We considered model performance statistically significant when the 95% confidence interval did not include zero, indicating that the observed associations are unlikely to have occurred by chance.

We chose bootstrapping over permutation testing because, while both can assess statistical significance, bootstrapping additionally provides uncertainty estimates in the form of confidence intervals. Given the large sample size in our study, significance testing can be less informative, as even small effects may reach statistical significance. Bootstrapping offers a more nuanced understanding of model uncertainty.

Line 233: “To evaluate model performance and assess statistical significance, we aggregated the predicted and observed g-factor values from each outer-fold test set. We then computed a bootstrap distribution of Pearson’s correlation coefficient (r) by resampling with replacement 5 000 times, generating 95% confidence intervals (CIs) (Fig. 1). Model performance was considered statistically significant if the 95% CI did not include zero, indicating that the observed associations were unlikely to have occurred by chance.”

(2) Applying and testing the trained models on an external validation set would increase confidence in generalisability of the model.

We appreciate this excellent suggestion. While we considered this approach, implementing it would require identifying an appropriate external dataset with comparable neuroimaging and behavioural measures, along with careful matching of acquisition protocols and variable definitions across sites. These challenges extend beyond the scope of the current study, though we fully agree that this represents an important direction for future research.

Our findings, obtained from one of the largest neuroimaging datasets to date with training and test samples exceeding most previous studies, align closely with existing literature: the predictive accuracy of each neuroimaging phenotype and modality for cognition matches the effect size reported in meta-analyses (r ≈ 0.4; e.g., Vieira et al., 2020). The ability of dwMRI, rsMRI and sMRI to capture the cognition-mental health relationship is, in turn, consistent with our previous work in pediatric populations (Wang et al., 2025; Pat et al., 2022).

Vieira S, Gong QY, Pinaya WHL, et al. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull. 2020;46(1):17-26.

Wang Y, Anney R, Pat N. The relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis. eLife. 2025.14:RP105537.

Pat N, Wang Y, Anney R, Riglin L, Thapar A, Stringaris A. Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. Hum Brain Mapp. 2022;43:5520–5542.

(3) The rationale for selecting a 5-by-10-fold cross-validation scheme is not clearly explained. Clarifying why this structure was preferred over more commonly used alternatives, such as 10-by-10 or 5-by-5 cross-validation, would strengthen the methodological transparency.

Thank you for this important methodological question. Our choice of a 5-by-10-fold crossvalidation scheme was motivated by the need to balance robust hyperparameter tuning with computational efficiency, particularly memory and processing time. Retaining five outer folds allowed us to rigorously assess model performance across multiple data partitions, leading to an outer-fold test set at least n = 4 000 and providing a substantial amount of neuroimaging data involved in model training. In contrast, employing ten inner folds ensured robust and stable hyperparameter tuning that maximizes the reliability of model selection. Thus, the 5-outer-fold with our large sample provided sufficient out-of-sample test set size for reliable model evaluation and efficient computation, while 10 inner folds enabled robust hyperparameter tuning. We now provide additional rationale for this design decision on Page 10.

Line 188: “We employed nested cross-validation to predict cognition from mental health indices and 72 neuroimaging phenotypes (Fig. 1). Nested cross-validation is a robust method for evaluating machine-learning models while tuning their hyperparameters, ensuring that performance estimates are both accurate and unbiased. Here, we used a nested cross-validation scheme with five outer folds and ten inner folds.

We started by dividing the entire dataset into five outer folds. Each fold took a turn being held out as the outerfold test set (20% of the data), while the remaining four folds (80% of the data) were used as an outer-fold training set. Within each outer-fold training set, we performed a second layer of cross-validation – this time splitting the data into ten inner folds. These inner folds were used exclusively for hyperparameter tuning: models were trained on nine of the inner folds and validated on the remaining one, cycling through all ten combinations.

We then selected the hyperparameter configuration that performed best across the inner-fold validation sets, as determined by the minimal mean squared error (MSE). The model was then retrained on the full outer-fold training set using this hyperparameter configuration and evaluated on the outer-fold test set, using four performance metrics: Pearson r, the coefficient of determination ( R²), the mean absolute error (MAE), and the MSE. This entire process was repeated for each of the five outer folds, ensuring that every data point is used for both training and testing, but never at the same time. We opted for five outer folds instead of ten to reduce computational demands, particularly memory and processing time, given the substantial volume of neuroimaging data involved in model training. Five outer folds led to an outer-fold test set at least n = 4 000, which should be sufficient for model evaluation. In contrast, we retained ten inner folds to ensure robust and stable hyperparameter tuning, maximising the reliability of model selection.”

(4) A more detailed discussion of which specific brain regions or features within each neuroimaging modality contributed most strongly to the prediction of cognition would enhance neurobiological relevance of the findings.

Thank you for this thoughtful suggestion. To address this point, we have included feature importance plots for the top-performing neuroimaging phenotypes within each modality (Figure 5 and Figures S2–S4), demonstrating the relative contributions of individual features to the predictive models. While we maintain our primary focus on cross-modality performance comparisons in the main text, as this aligns with our central aim of evaluating multimodal MRI markers at the integrated level, we outline the contribution of neuroimaging features with the highest predictive performance for cognition in the revised Results and Discussion.

Methods

Line 255: “To determine which neuroimaging features contribute most to the predictive performance of topperforming phenotypes within each modality, while accounting for the potential latent components derived from neuroimaging, we assessed feature importance using the Haufe transformation [62]. Specifically, we calculated Pearson correlations between the predicted g-factor and scaled and centred neuroimaging features across five outer-fold test sets. We also examined whether the performance of neuroimaging phenotypes in predicting cognition per se is related to their ability to explain the link between cognition and mental health. Here, we computed the correlation between the predictive performance of each neuroimaging phenotype and the proportion of the cognition-mental health relationship it captures. To understand how demographic factors, including age and sex, contribute to this relationship, we also conducted a separate set of commonality analyses treating age, sex, age², age×sex, and age²×sex as an additional set of explanatory variables (Fig. 1).”

Results

dwMRI

Line 331: “Overall, models based on structural connectivity metrics performed better than TBSS and probabilistic tractography (Fig. 3). TBSS, in turn, performed better than probabilistic tractography (Fig. 3 and Table S13). The number of streamlines connecting brain areas parcellated with aparc MSA-I had the best predictive performance among all dwMRI neuroimaging phenotypes (R²_mean = 0.052, r_mean = 0.227, 95% CI [0.212, 0.235]). To identify features driving predictions, we correlated streamline counts in aparc MSA-I parcellation with the predicted g_factor values from the PLSR model. Positive associations with the predicted _g-factor were strongest for left superior parietal-left caudal anterior cingulate, left caudate-right amygdala, and left putamen-left hippocampus connections. The most marked negative correlations involved left putamen-right posterior thalamus and right pars opercularis-right caudal anterior cingulate pathways (Fig. 5 and Supplementary Fig. S2).”

rsMRI

Line 353: “Among RSFC metrics for 55 and 21 ICs, tangent parameterization matrices yielded the highest performance in the training set compared to full and partial correlation, as indicated by the cross-validation score. Functional connections between the limbic (IC10) and dorsal attention (IC18) networks, as well as between the ventral attention (IC15) and default mode (IC11) networks, displayed the highest positive association with cognition. In contrast, functional connectivity between the limbic (IC43, the highest activation within network) and default mode (IC11) and limbic (IC45) and frontoparietal (IC40) networks, between the dorsal attention (IC18) and frontoparietal (IC25) networks, and between the ventral attention (IC15) and frontoparietal (IC40) networks, showed the highest negative association with cognition (Fig. 5 and Supplementary Fig. S3 and S4)”

sMRI

Line 373: “FreeSurfer subcortical volumetric subsegmentation and ASEG had the highest performance among all sMRI neuroimaging phenotypes (R²_mean = 0.068, r_mean = 0.244, 95% CI [0.237, 0.259] and R²_mean = 0.059, r_mean = 0.235, 95% CI [0.221, 0.243], respectively). In FreeSurfer subcortical volumetric subsegmentation, volumes of all subcortical structures, except for left and right hippocampal fissures, showed positive associations with cognition. The strongest relations were observed for the volumes of bilateral whole hippocampal head and whole hippocampus (Fig. 5 and Supplementary Fig. S5 for feature importance maps). Grey matter morphological characteristics from ex vivo Brodmann Area Maps showed the lowest predictive performance (R²_mean = 0.008, r_mean = 0.089, 95% CI [0.075, 0.098]; Fig. 3 and Table S15).”

Discussion

dwMRI

Line 562: “Among dwMRI-derived neuroimaging phenotypes, models based on structural connectivity between brain areas parcellated with aparc MSA-I (streamline count), particularly connections with bilateral caudal anterior cingulate (left superior parietal-left caudal anterior cingulate, right pars opercularis-right caudal anterior cingulate), left putamen (left putamen-left hippocampus, left putamen-right posterior thalamus), and amygdala (left caudate-right amygdala), result in a neural indicator that best reflects microstructural resources associated with cognition, as indicated by predictive modeling, and more importantly, shares the highest proportion of the variance with mental health-g, as indicated by commonality analysis.”

rsMRI

Line 583: “We extend findings on the superior performance of rsMRI in predicting cognition, which aligns with the literature [15, 28], by showing that it also explains almost a third of the variance in cognition that mental health captures. At the rsMRI neuroimaging phenotype level, this performance is mostly driven by RSFC patterns among 55 ICA-derived networks quantified using tangent space parameterization. At a feature level, these associations are best captured by the strength of functional connections among limbic, dorsal attention and ventral attention, frontoparietal and default mode networks. These functional networks have been consistently linked to cognitive processes in prior research [127–130].”

sMRI

Line 608: “Integrating information about brain anatomy by stacking sMRI neuroimaging phenotypes allowed us to explain a third of the link between cognition and mental health. Among all sMRI neuroimaging phenotypes, those that quantified the morphology of subcortical structures, particularly volumes of bilateral hippocampus and hippocampal head, explain the highest portion of the variance in cognition captured by mental health. Our findings show that, at least in older adults, volumetric properties of subcortical structures are not only more predictive of individual variations in cognition but also explain a greater portion of cognitive variance shared with mental health than structural characteristics of more distributed cortical grey and white matter. This aligns with the Scaffolding Theory that proposes stronger compensatory engagement of subcortical structures in cognitive processing in older adults [138–140].”

(5) The formatting of some figure legends could be improved for clarity - for example, some subheadings were not formatted in bold (e.g., Figure 2 c)

Thank you for noticing this. We have updated the figures to enhance clarity, keeping subheadings plain while bolding figure numbers and MRI modality names.

By applying this level of critical thinking it is likely that this practice will help a reader remember the text better.

It is important to put yourself into others perspective when writing.

Version 4 of this preprint has been peer-reviewed and recommended by Peer Community in Infections.<br /> See the peer reviews and the recommendation.

eLife Assessment

This valuable paper investigates how fish avoid thermal disturbances that occur on fast timescales. The authors use a creative experimental approach that quickly creates a vertical thermal interface, which they combine with careful behavioral analyses. The evidence supporting their results is solid, but there is a potential confounding factor between temperature and vertical positioning, and characterization of the thermal interface would greatly assist in interpreting the results.

Reviewer #1 (Public review):

Summary:

The experiment is interesting and well executed and describes in high detail fish behaviour in thermally stratified waters. The evidence is strong but the experimental design cannot distinguish between temperature and vertical position of the treatments.

Strengths:

High statistical power, solid quantification of behaviour.

Weaknesses:

A major issue with the experimental design is the vertical component of the experiment. Many thermal preference and avoidance experiments are run using horizontal division in shuttlebox systems or in annular choice flumes. These remove the vertical stratification component so that hot and cold can be compared equally, without the vertical layering as a confounding factor. The method chosen, with its vertical stratification, is inherently unable to control for this effect because warm water is always above, and cold water is always below. This complicates the interpretations.

Reviewer #2 (Public review):

The paper by Naudascher et al., investigates an interesting question: How do fish react to and avoid thermal disturbances from the optimum that occur on fast timescales. Previous work has identified potential strategies of warm avoidance in fish on short timescales while strategies for cold avoidance are far more elusive. The work combines a clever experimental paradigm with careful analysis to show that trout parr avoid cold water by limiting excursions across a warm-cold thermal interface. While direct measurements of the interface are lacking, thermal dynamics simulations suggest that trout parr avoid the warm-cold interface in the absence of gradient information.

The authors assume that the thermal interface triggers the upward turning behavior, possibly leading to the formation of an associative memory. However, an alternative explanation is that exposure to cold water during initial excursions increases the tendency for upward turns. In other words, exposure to a cold interface changes the behavioral state leading to increases in gravity controlled upward turning. This could be an adaptive strategy since for temperatures > 4C swimming upwards is a good strategy to reach warmer water. That being said, the vertical design offers new insight and is ecologically relevant.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Summary:

The experiment is interesting and well executed and describes in high detail fish behaviour in thermally stratified waters. The evidence is strong but the experimental design cannot distinguish between temperature and vertical position of the treatments.

Strengths:

High statistical power, solid quantification of behaviour.

Weaknesses:

A major issue with the experimental design is the vertical component of the experiment. Many thermal preference and avoidance experiments are run using horizontal division in shuttlebox systems or in annular choice flumes. These remove the vertical stratification component so that hot and cold can be compared equally, without the vertical layering as a confounding factor. The method chosen, with its vertical stratification, is inherently unable to control for this effect because warm water is always above, and cold water is always below. This complicates the interpretations and makes firm conclusions about thermal behaviour difficult.

We highly appreciate this evaluation and have addressed the reviewer’s specific comments below.

The sentence "Further, the metabolic performance (and thus functions including growth, reproduction, and locomotion) of ectotherms takes the form of a bell-shaped curve as a function of temperature6, peaking within a range of optimal temperatures (the 'preferendum') and going to zero at lower and upper temperature limits7." contains several over-simplifications and misconceptions:

(1) Thermal performance curves are never bell-shaped.

(2) The optimum for various traits often shows different TPCs.

(3) The preferendum rarely lines up with the thermal optimum for various trait TPCs.

(4) Performance for various traits rarely reaches zero at upper or lower limits, instead they can reach zero at less extreme temperatures (e.g. growth) or maintain high function all the way up to and sometimes beyond thermal limits (e.g. aerobic scope, heart rate).

We highly appreciate this input. We have replaced that sentence with: L69-71: “Because temperature influences the rates of most physiological processes, rapid warming or cooling can affect fish performance traits, including metabolic rates, swimming ability, and thermal tolerance (Jutfelt et al. 2024).”

The use of adaptation instead of acclimation is confusing. Adaptation should be reserved for evolutionary change. This is an issue in several parts of the manuscript.

Thanks for this input, we have replaced the word adapt with acclimate in two instances: L79 and L398.

It is not true that "very few quantitative studies of thermotaxis have been conducted in fish". There exists an extensive literature on thermal preference and avoidance in fish that the manuscript downplays.

Thanks a lot for this input. We understand that thermal preference is ultimately driven by mechanistic responses to thermal gradients, and that thermotaxis and thermokinesis are the two mechanisms used by fish to navigate heterothermal environments. Our study and analysis are focused on understanding these mechanisms in vertically stratified conditions, not to understand thermal preferences per se. We have modified our text to clarify this aspect. Our literature review was focused on the behavioral mechanisms and our understanding is that the establishment of thermal preferences has a different goal compared to understanding how fish respond to rapid changes in water temperature. We have deleted that sentence and replaced it by (L107-110): “While the thermal preference of fish is a well-established field of research, very few quantitative studies of the behavioral mechanisms allowing fish to seek their preferendum (i.e. thermotaxis) have been conducted in fish.”

(Methods) It is unclear why the blue dye was used in all experiments. The fish can see the differently coloured water layer and that may have affected their choices. Five control trials without dye were run but finding no difference there could also be due to low statistical power.

We appreciate this comment. The blue dye was used to visualize the precise location of the thermal interface and was therefore necessary in all experiments (see Methods section ‘Visualization and evolution of the thermal interface’). We acknowledge that fish can perceive the colored water layer, but since the dye concentration and resulting color intensity were consistent across all treatments, we do not see how it could have acted as a confounding variable. While we recognize the possibility of some behavioral influence from the dye, the clear behavioral differences across treatments indicate that it was not a determining factor. To emphasize this we have added the following to the manuscript (L701-703): “Furthermore, because the dye concentration and resulting color intensity were consistent across all treatments, the dye did not act as a confounding variable in our statistical comparisons.”

Regarding statistical power, our control experiment without dye (N = 16 fish, 4 replicates; see Fig. S34 and S35) provides sufficient statistical power to assess whether the dye influenced behavior. The reviewer indicated that the high statistical power was a strength of the paper, which aligns with our view that our study design enables robust statistical comparisons. It seems contradictory that statistical power is a concern for the control trials, given that our main experiments were conducted with a similar sample size. Indeed, the number of replicates used is consistent with similar studies and balances statistical rigor with the ethical goal of reducing the number of animals used in experimentation. To emphasize this, we have added the following to the manuscript (L865-868): “The number of replicates used in this study reflects a balance between statistical rigor and the ethical imperative to minimize the use of animals in experimentation. Regarding statistical power, our design (five replicates with groups of four fish each) is consistent with similar studies and represents an adequate sample size.”

A major issue with the experimental design is the vertical component of the experiment. Many thermal preference and avoidance experiments are run using horizontal division in shuttlebox systems or in annular choice flumes. These remove the vertical stratification component so that hot and cold can be compared equally, without the vertical layering as a confounding factor. The method chosen, with its vertical stratification, is inherently unable to control for this effect because warm water is always above, and cold water is always below. This complicates the interpretations and makes firm conclusions about thermal behaviour difficult. This issue should be thoroughly discussed.

Thank you very much for this comment. We revised the manuscript accordingly, to clearly indicate that our goal was to assess the response of fish to vertically thermally stratified water, a scenario that occurs frequently in nature. We have added the following paragraph the discussion (L523-530): “However, a generalization of our observations to horizontally oriented thermal gradients remains elusive. Our results are inherently tied to the vertical stratification created in our experiments. As warm water was always positioned above and cold water below, we could not control for the effect of vertical position (i.e., we could not do cold over warm layer experiments). This limits our ability to directly compare our findings to those obtained from horizontally oriented thermal gradients. On the other hand, the case we addressed is of direct environmental relevance, as natural waters often experience vertical thermal stratification.”

It is unclear why the authors assume an "optimal temperature" (undefined for which trait) of 12°C for brown trout parr, and why they assume the preference temperature would match that "optimal" temperature. The thermal biology for any fish species is more complex than a single perfect temperature, with various traits showing differing optima and often a mismatch with the preferred temperature. The literature suggests brown trout growth optima between 13 and 16°C, and preference temperature has even been suggested to be as high as 21°C. In light of this, the authors' conclusion that brown trout avoid cold and don't avoid warm water is possibly misguided. It is possible that the brown trout had a preference temperature higher than 12°C, which should be acknowledged and discussed.

This is indeed a very important aspect, which was partly (but indeed not fully) already addressed in the discussion. To reflect these considerations, we have expanded the existing paragraph in the discussion (additions are in yellow). (L422 - L439): “We conclude from the behavior of fish when warmer water was available that their acute thermal preferendum exceeded 12 °C, departing from the acclimation temperature we had chosen based on the thermal preferendum for trout reported in literature[33]. Indeed, the thermal biology for any fish species is more complex than a single, static thermal preferendum: Many internal and external factors, such as hypoxia, satiation, time of day, and life stage[5], can influence the temperature preference of fish. For example, the level of satiation can have an impact because when fish are well fed, their growth rate increases with body temperature as metabolic performance increases[40]. This modifies the preferred temperature, as observed in Bear Lake sculpin (Cottus extensus) that ascend into warmer water after feeding to stimulate digestion and thereby achieve a three-fold higher growth rate[41]. In contrast, field studies with adult fish have observed movement from warm to cold water in summer[42,43], allowing fish to lower their metabolic rate, likely in effort to conserve energy[2,44]. We propose that the behavior of trout parr upon exposure to warmer water in our experiments served to achieve a higher body temperature to ultimately increase growth rate, which is critical for this life stage[45,46]. Indeed, growth experiments on brown trout populations have shown that optimal growth temperatures can range between 15 and 19 °C, depending on the stream of origin[46].”

The figures are unnecessarily complex and introduce a long list of abbreviations and Greek characters for no apparent reason. There are many simpler ways for showing the results so unclear why they are so opaque.

We appreciate the reviewer’s feedback and agree on the importance of clarity, however (in the absence of specific suggestions) we did not make changes to the figures or the use of Greek characters (which align with convention), as we believe they effectively convey the results. We highlight that the data themselves are very rich (multiple fish, multiple phases, multiple treatments, etc.) and we wanted to convey this richness in a compact and transparent manner.

Reviewer #2:

This paper investigates an interesting question: how do fish react to and avoid thermal disturbances from the optimum that occur on fast timescales? Previous work has identified potential strategies for warm avoidance in fish on short timescales while strategies for cold avoidance are far more elusive. The work combines a clever experimental paradigm with careful analysis to show that trout parr avoid cold water by limiting excursions across a warm-cold thermal interface. While I found the paper interesting and convincing overall, there are a few omissions and choices in the presentation that limit interpretability and clarity.

A main question concerns the thermal interface itself. The authors track this interface using a blue dye that is mixed in with either colder or warmer water before a gate is opened that leads to gravitational flow overlaying the two water temperatures. The dye likely allows to identify convective currents which could lead to rapid mixing of water temperatures. However, it is less clear whether it accurately reflects thermal diffusion. This is problematic as the authors identify upward turning behavior around the interface which appears to be the behavioral strategy for avoiding cold water but not warm water. Without knowing the extent of the gradient across the interface, it is hard to know what the fish are sensing. The authors appear to treat the interface as essentially static, leading them to the conclusion that turning away before the interface is reached is likely related to associative learning. However, thermal diffusion could very likely create a gradient across centimeters which is used as a cue by the fish to initiate the turn. In an ideal world, the authors would use a thermal camera to track the relationship between temperature and the dye interface. Absent that, the simulation that is mentioned in passing in the methods section should be discussed in detail in the main text, and results should be displayed in Figure 1. Error metrics on the parameters used in the simulation could then be used to identify turns in subsequent figures that likely are or aren't affected by a gradient formed across the interface.

The authors assume that the thermal interface triggers the upward-turning behavior. However, an alternative explanation, which should be discussed, is that cold water increases the tendency for upward turns. This could be an adaptive strategy since for temperatures > 4C turning swimming upwards is likely a good strategy to reach warmer water.

The paper currently also suffers from a lack of clarity which is largely created by figure organization. Four main and 38 supplemental figures are very unusual. I give some specific recommendations below but the authors should decide which data is truly supplemental, versus supporting important points made in the paper itself. There also appear to be supplemental figures that are never referenced in the text which makes traversing the supplements unnecessarily tedious.

The N that was used as the basis for statistical tests and plots should be identified in the figures to improve interpretability. To improve rigor, the experimental procedures should be expanded.

Specifically, the paper uses two thermal models which are not detailed at all in the methods section.

We appreciate these crucial comments to our paper. We have addressed these points in detail below.

As stated above, a characterization of the thermal interface is critical. Ideally via measurement or at least by expanding on the simulation.

We appreciate the idea of using thermal cameras and, indeed, we had initially tried to use them. However, thermal cameras generally cannot see through plexiglass or glass-like material due to the way infrared radiation interacts with these materials. While thin plastics can transmit some infrared, thicker plastics and reflective materials like glass tend to block or reflect infrared light.

We have attempted to better characterize the thermal interface thickness, namely the spatial extent of the thermal gradient over the time period of our experiments (20 min). Indeed, our simulations in the original SI were conducted precisely to estimate the thermal interface thickness, though based on thermal diffusion in still water, while turbulence generated by the moving gravity current can smear out the interface, particularly in the initial phase. To account for this in our in the reviewed manuscript, we adopted a phenomenological approach to estimate the initial increase in thickness of the thermal interface due to turbulence and present this refined simulation in our manuscript.

Our analysis suggests that, rather than assuming an initial interface thickness of zero (as in the original version of the manuscript), the thermal diffusion simulations should begin with an initial thickness of 2.8 mm in TR1. To incorporate this adjustment, we set the initial interface thickness to 2.8 mm and ran the simulation forward for t = 20 min, assuming diffusion. This approach resulted in a final interface thickness ranging between 4 and 6 cm (see Fig. 29 in the Supplementary Information).

To reflect this refinement, we have added a new paragraph (L717-758: "Characterization of the thermal gradient", to the Methods section. Additionally, we have updated Fig. S29 in the Supplementary Information and included an average (over time and across treatments) gradient thickness of 5 cm in Figs. 2 and 3 of the manuscript. The revised Figs. 2 and 3 now explicitly indicate the estimated vertical extent of the thermal gradient, with an extended caption detailing these changes.

The simulation should be detailed in the methods so that its validity can be evaluated and ideally, it should involve curved interfaces as encountered in the experiment.

To account for the effect of turbulence during the initial, inertia-dominated phase after the gate removal, we have provided a correction for the initial thickness of the interface (see the addition to the Methods section). Thank you for your suggestion regarding the incorporation of curved interfaces in the simulations. We believe that including curved interfaces in the simulations would not significantly affect the results. As shown in the manuscript, the interface is curved primarily during the initial phase of the process (first 2 min where the flow is inertia-dominated), which is currently not included in our data analysis (phase 1 begins 2 min after the gate removal).

In that vein, distances from the interface rather than height above the interface should be reported for the fish.

We acknowledge the reviewer’s suggestion to report distances from the interface rather than height above or below it. However, beyond the initial phase, we do not see a strong justification for using the orthogonal distance over the vertical distance, as the choice is inherently arbitrary (e.g., one could also measure the distance along the fish’s orientation vector). We have therefore kept our assessment based on the vertical distance.

Absent measurements, the paragraph on associative learning should be struck from the discussion as it is purely speculative.

We agree that the original paragraph on associative learning may have sounded overly speculative. However, after updating our manuscript with additional simulations of the thermal gradient's vertical extent, we found that fish perform upward turns not only above the thermal interface, but also before entering the thermal gradient itself. This observation makes us hesitant to attribute the response solely to thermotaxis. We believe it is essential to provide a plausible explanation—albeit speculative—for how fish initiate these turns before directly encountering the cold-water gradient. To support this, we have extended the discussion in this paragraph and added Supplementary Fig. 39. The new text now reads (additions in yellow): (L487 – 499): “Our findings show that fish were able to perform upward turns while still located above the thermal interface and that is, before actually sampling the cold water below the interface. In fact, our simulation of the vertical extent of the thermal gradient revealed that a substantial fraction of upward turns occurred before fish encountered the gradient itself — that is, prior to any sensory detection of the temperature change (Supplementary Fig. 39). This finding may be evidence of associative learning, whereby fish used information regarding the presence of colder water at depth obtained at prior times. While the current data do not provide conclusive evidence in this regard, they prompt the possibility that, rather than responding solely to immediate thermal cues, fish use spatial memory or associative learning to anticipate the location of colder water based on prior experience. Indeed, fish are able to perform associative learning based on non-visual cues[53], create mental maps of their surroundings54 and retain memory for hours[55], days[56] and months[57,58].”  

The body-temperature simulations need to be detailed in the methods.

Thanks for this comment. We have removed the supplementary text section and have included the paragraph “Body cooling during cold-water excursions” into the methods section of our manuscript (L804 - L829).

Constant temperature experiments could be helpful in addressing the importance of a gradient/interface for triggering upward turning

We agree, however, we were limited (for ethical reasons) to a maximum number of fish we could use in the experiments. Hence, we focused on getting approval to run experiments focused on the responses to thermal gradients. However, occupancy during the acclimation phase in 12 °C showed that fish were much more stationary and primarily occupied the lower half of the tank.

A lot of ease of reading could be gained by labeling the conditions according to either the second temperature or perhaps even better the delta temperature (i.e. TR[-2C] instead of TR1).

We agree that labeling conditions by the second temperature or delta temperature could in principle improve readability. However, since T_bottom and T_top are explicitly mentioned in each main figure at least once, they can be directly associated with the respective treatment. Therefore, we have opted to retain the current labeling for consistency.

The figure legends are often short and do not accurately label all figure elements. This is especially true for supplemental figure legends which often appear rushed (e.g., the legend for Figure S2 stops mid-sentence, the legend of Figure S3 does not indicate what Ttop or Tbottom are).

We appreciate the reviewer’s comment and have carefully revised all figure legends to ensure clarity and completeness. Specifically, we have corrected figure labels, expanded the descriptions for supplemental figures, and ensured that all elements are accurately defined. For instance, we have completed the legend for Figure S2 and clarified the definitions of T_top and T_bottom in Figure S3. Additionally, we have systematically reviewed all figure legends to prevent inconsistencies and omissions.

For Figure S3, to improve clarity, plotting the standard deviation at different points in the tank across the phases could be more informative than the hard-to-distinguish multi-line plots in different shades of red.

We appreciate the reviewer’s suggestion regarding Figure S3. However, the primary goal of this figure is to illustrate how the thermal interface moves over time. While plotting the standard deviation at different points in the tank could provide additional statistical insights, it would detract from the intended visualization of the interface dynamics. For this reason, we have opted to retain the current multi-line representation. Nevertheless, we have ensured that the figure is as clear as possible by refining the color contrast and improving the legend for better readability.

There is an inconsistency in in-text citation styles (mixture of superscript and numbers in brackets).

Thank you for pointing this out. We have carefully reviewed the manuscript and corrected any inconsistencies in the in-text citation style to ensure uniform formatting throughout.

While the statement in the introduction, that increases in movement frequency could be purely metabolic in nature is correct, at least for larval zebrafish it has been shown that sensory neural activity is predictive of motor neuron activity and swim rates (Haesemeyer, 2018, cited by the authors).

This is an interesting finding. It is however unclear to us why this information is crucial in our context of brown trout parr.

Examples of summary results from Supplementary Figures 8-10 should be bundled in a main text figure since this appears to be important information supporting the conclusions.

We agree that Supplementary Figures 8–10 contain important information (i.e. Boxplots) on vertical occupancy and the time individuals spent in different water temperatures. However, this information is already integrated into Figure 2C, D, F, and G, which display the vertical distributions of fish across treatments and over time. Given the current length of the manuscript, adding another main-text figure could dilute rather than enhance clarity. For this reason, we have opted to keep these details in the Supplementary Materials while ensuring they are appropriately referenced in the main text.

The distributions of excursion length for all treatments should be graphed in a main figure to support the point made in the third paragraph of the "Trout parr... do not avoid warm water" section of the results.

We appreciate the reviewer’s suggestion. However, we do not believe that plotting excursion length is necessary to support this statement, as the key finding is already well represented in the manuscript. Specifically, the transition to bimodal depth occupancy, with fish spending comparable time above and below the interface in warm-water treatments (TR6–TR9), is clearly conveyed in Figure 2F and Supplementary Figure 8B. Additionally, this information is explicitly stated in the results section (L235): "Fish did not avoid warmer water in any of the warm-water treatments (TR6–TR9). Instead, fish transitioned to a bimodal depth occupancy, with comparable time spent above and below the interface (Fig. 2F; Supplementary Fig. 8B)." Given this, we believe that adding an additional figure would not enhance clarity but may instead introduce redundancy.

There should be a main figure panel that statistically compares the turn biases around the interface for the different conditions and the +/- 5cm interface line mentioned in the text should be visualized in the appropriate figures - incidentally, this length scale is on par with the diffusion seen in simulations further suggesting that fish in fact sense a gradient here rather than remembering an interface.

To address the reviewer’s comment, we have made the following updates:

• Extended and incorporated simulations of the thermal interface thickness (see Methods and Supplementary Fig. 29).

• Plotted the vertical locations of up-turning events relative to the phase-averaged position of the thermal interface (see Supplementary Fig. 39), which includes the estimated 5 cm vertical extent of the thermal gradient.

• Added the thermal interface thickness to the main figures (Fig. 3F,G and Fig. 2E,H) where applicable.

While we do not claim that memory alone explains cold-water avoidance, our data still suggests that it may contribute to the observed behavior, particularly since a substantial number of upturns occurred before the fish entered the thermal gradient (see also Author response image 1 below). Our aim is not to statistically disentangle the relative contribution of thermotaxis versus associative learning, but to propose a plausible interpretation of this observed anticipatory behavior with due caution to clarify that this is only a possibility.

Given that the thermal gradient is now visualized and characterized in detail, we respectfully suggest that an additional statistical comparison of turn biases would not add further clarity. We believe that is is evidence that vertical turning, away from the cold, occurred within and above the thermal gradient. However, we welcome the reviewer’s perspective and to demonstrate that turning points occur outside and above the thermal interface we have plotted them against gradient growth over time (see Author response image 1 below).

Author response image 1.

The colored area indicates the temporal growth of thermal interface thickness.

Reviewer #3:

In this study, the authors measured the behavioural responses of brown trout to the sudden availability of a choice between thermal environments. The data clearly show that these fish avoid colder temperatures than the acclimation condition, but generally have no preference between the acclimation condition or warmer water (though I think the speculation that the fish are slowly warming up is interesting). Further, the evidence is compelling that avoidance of cold water is a combination of thermotaxis and thermokinesis. This is a clever experimental approach and the results are novel, interesting, and have clear biological implications as the authors discuss. I also commend the team for an extremely robust, transparent, and clear explanation of the experimental design and analytical decisions. The supplemental material is very helpful for understanding many of the methodological nuances, though I admit that I found it overwhelming at times and wonder if it could be pruned slightly to increase readability. Overall, I think the conclusions are generally well-supported by the data, and I have no major concerns.

Minor comments

P2 intro paragraphs 1/3 - it is not clear that thermal preference generally reflects the thermal optimum, partly because it is not clear what trait is being optimized (fitness?). Some nuance here would be helpful, and would also link nicely to the discussion on p10.

Thank you for this comment. We have now refined this section as follows (L67–71): "As most fish species are ectotherms, their body temperature fluctuates with the surrounding water temperature. Because temperature influences the rates of most physiological processes, rapid warming or cooling can affect fish performance traits, including metabolic rates, swimming ability, and thermal tolerance[6]."

To further clarify how thermal preference relates to thermal optimum and what trait is being optimized, we have incorporated additional nuance in this section. Specifically, we now acknowledge that thermal preference may not always align with the thermal optimum for performance or fitness.

P2 intro paragraph 2 - "adapt physiologically" implies evolution, but here you are referring to plasticity. Suggest saving the word "adapt/adaptation" for evolutionary changes (see also p9).

Thank you for this comment. We have revised the wording to "acclimate physiologically" (L79) to more accurately reflect plastic responses rather than evolutionary adaptation.

P7 - "This difference in probabilities (ρup - ρdown) was particularly large in the region immediately above and below the interface (-5 cm < D < 5 cm; Fig. 3F) and is a hallmark of a thermotactic behavior." I agree that the result provides compelling evidence for thermotaxis, but would it be possible to bolster this case by statistically testing for a difference in probabilities among the treatment groups here?

In addition to Fig. 3F, we are presenting statistical evidence that for colder water temperatures, fish penetrate less deeply into the cold lower water. The decreasing trend was statistically significant (Mann–Kendall test: , p < 0.001; Supplementary Table 6) and is presented in Fig. 4C. The depth reached during each cold-water excursion is determined by the location of the vertical turning point, which redirects the fish upward toward the surface. We think this is sufficient evidence for thermotaxis.

P9 paragraph 3 = "recent studies suggest that fish may instead respond to temporal changes of their internal body temperature." It seems like a citation is missing here. Would be useful to briefly summarize the evidence for internal temperature sensing that is the basis of this modelling exercise.

Thanks, we have added that citation (L385).

P10 "Our findings provide the first experimental evidence for this mode of behavioral thermoregulation in which fish navigate their heterothermal environment to achieve gradual body warming."

I think this statement overreaches given the presented data. While there may be a trend towards fish in the warm treatment spending increasing amounts of time in the upper half of the tank, I do not see this pattern supported statistically. There is also no evidence of gradual body warming, and even if there was I disagree that this would constitute experimental evidence that this was happening "intentionally". By this reasoning, any shuttlebox experiment in which fish actively shuttle between relatively warm and cool sides to end up with a preference that is above the starting condition would also constitute evidence for gradual warming. Overall, this is an interesting pattern, but I do not think there is sufficient evidence to conclude that fish are strategically warming.

We appreciate the reviewer’s comment and acknowledge that our original wording may have overstated the evidence. We have revised the sentence to better reflect the evdience presented (L411-415): “Our observations resemble this mode of behavioral thermoregulation, in which fish progressively favor warmer regions within a heterothermal environment. However, additional experimental evidence is required to determine the mechanisms underlying this behavior.”

P11 "Despite the avoidance response of cold water, fish engaged in repeated cold-water excursions..."

This is an interesting speculation, but I think it would be helpful to also point out that these fish are biased towards the bottom of the tank (based on control measurements) and this pattern may therefore simply reflect a desire to be lower in the water column.

Thank you for this helpful comment. We have now added this point to the revised text, which reads (L475-477): “Despite the avoidance response to cold water, fish engaged in repeated cold-water excursions, potentially reflecting a behavioral strategy to map the thermal environment. This pattern may also reflect an inherent tendency to occupy the lower part of the tank, as observed during homogeneous temperature of 12 °C during the acclimation phase.”

P13 - why was the dye always added to the right side of the tank, instead of being assigned to a side randomly? I think the control experiment is good evidence that the dye did not substantially affect behaviour, but it seems like it would have been nice to separate dye and novel temperature exposure.

We agree that randomizing the side of dye application would have been ideal. The dye was consistently added to the right side to maintain procedural consistency, ensuring that the “incoming” or “novel” temperature was always dyed. That said, our control experiment provides strong evidence that the dye itself did not influence behavior (as discussed above and in the manuscript).

There is not intercept in the DGP but the regression includes one; is this intentional?

eLife Assessment

This important study uses the delay line axon model in the chick brainstem auditory circuit to examine the interactions between oligodendrocytes and axons in the formation of internodal distances. This is a significant and actively studied topic, and the authors have used this preparation to support the hypothesis that regional heterogeneity in oligodendrocytes underlies the observed variation in internodal length. In a solid series of experiments, the authors have used enhanced tetanus neurotoxin light chains, a genetically encoded silencing tool, to inhibit vesicular release from axons and support the hypothesis that regional heterogeneity among oligodendrocytes may underlie the biased nodal spacing pattern in the sound localization circuit.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #2 (Public review):

Summary:

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Major comments:

(1) The authors should test the efficiency of TeNT to validate that vesicular release is indeed inhibited from expressing neurons. Additionally, the authors should clarify if their TeNT expression system results in the whole tract being silenced, or results in sparse vesicular release inhibition in only a few neurons.

(2) The authors should revise their statistical analyses throughout, and supply additional information to explain the rationale for the statistical tests used, including e.g. data normality, paired sampling, number of samples/independent biological replicates.

(3) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the avian auditory circuit?

(4) The study shows a correlation between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). The authors should either include such experiments, or discuss their value in supporting the interpretation of their results.

(5) The authors should discuss very pertinent prior studies, in particular to contextualize their findings with (a) known neuron-autonomous modes of node formation prior to myelination, (b) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, (c) known correlation of myelin length and thickness with axonal diameter, (d) regional heterogeneity in the oligodendrocyte transcriptome.

Significance:

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

Reviewer #3 (Public review):

Summary:

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing.

Major comments:

(1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

(2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

(3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

Significance:

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

Comments on revised version:

This revised version is in large improved and the responses to reviewers' comments are generally relevant. However, the response regarding pre-nodes is not satisfactory. I understand that the authors prefer to avoid further experimentations, but I think this is an important point that needs to be clarified. Exploring stages between E12 and E15 are therefore of importance. When carefully examining some of the figures (Fig. 1E or 2D) I think that at E15 they may well be pre-nodes formation prior to myelin deposition, on structure the authors considered to be heminodes. To be convincing they should use double or triple labeling with, in addition to the nodal proteins (ankG and/or Nav pan), a good myelin marker such as antiPLP. The rat monoclonal developed by late Pr Ikenaka would give a sharper staining than the anti MAG they used. (I assume the clone must still be available in Okazaki ).

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Evidence, reproducibility and clarity

The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

(1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).

(2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

(1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.

Thanks for your suggestion to avoid confusion. We used the phrase "nodal spacing" instead of "nodal distribution" throughout the revised manuscript.

(2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?

As a key distinction, our study focuses specifically on the main trunk of the contralateral projection of NM axons. This projection features a sequential branching structure known as the delay line, where collateral branches form terminal arbors and connect to the ventral dendritic layer of NL neurons. This structural organization plays a critical role in influencing the dynamic range of ITD detection by regulating conduction delays along the NM axon trunk.

The study by Seidl et al. (2010) is a pioneering work that measured diameter of NM axon using electron microscopy, providing highly reliable data. However, due to the technical  limitations of electron microscopy, which does not allow for the continuous tracing of individual axons, it is not entirely clear whether the axons measured in the ventral NL region correspond to terminal arbors of collateral branches or the main trunk of NM axons (see Figure 9E, F in their paper). Instead, they categorized axon diameters based on their distance from NL cell layer, showing that axon diameter increases distally (see Figure 9G in their paper). Notably, the diameters of ventral axons located more than 120 μm away from the NL cell layer is almost identical to those in the midline.

As illustrated in our Figure 4D and Supplementary Video 2, the main trunk of the contralateral NM projection is predominantly located in these distal regions. Therefore, our findings complement those of Seidl et al. (2010) rather than contradicting them. We made this point as clear as possible in text (page 7, line 3).

(3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?

In this study, we examined chick embryos from E9 to just before hatching (E21) and post-hatch chicks up to P9. Chickens begin to perceive sound around E12 and possess sound localization abilities at the time of hatching (Grier et al., 1967) (added to page 4, line 9). Therefore, by E21, the sound localization circuit is largely established.

On the other hand, additional refinement of the circuit with aging is certainly possible. A key cue for sound localization, interaural time difference (ITD), depends on the distance between the two ears, which increases as the animal grows. As shown in Figure 2G, internodal length increased by approximately 20% between E18 and P9 while maintaining regional differences. Given that NM axons are nearly fully myelinated by E21 (Figure 4D, 6C), this suggests that myelin extends in proportion to the overall growth of the head and brain volume. We described this possibility in text (page 5, line 21)

Thus, our study covers not only the early stages of myelination but also the post-functional maturation in the sound localization circuit.

(4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

In this study, we demonstrated that although vesicular release did not affect internodal length, it selectively promoted oligodendrogenesis, thereby supporting the full myelination and hence the pattern of nodal spacing along the NM axons. We believe that this finding falls within the broader scope of 'activity-dependent plasticity' involving oligodendrocytes and nodes.

As summarized in the excellent review by Bonetto et al. (2021), activity-dependent plasticity in oligodendrocytes encompasses a wide range of phenomena, not limited to changes in internodal length but also including oligodendrogenesis. Moreover, the effects of neuronal activity are not uniform but likely depend on the diversity of both neurons and oligodendrocytes. For example, in the mouse visual cortex, activity-dependent myelination occurs in interneurons but not in excitatory neurons (Yang et al., 2020). Additionally, expression of TeNT in axons affected myelination heterogeneously in zebrafish; some axons were impaired in myelination and the others were not affected at all (Koudelka et al., 2016). In the mouse corpus callosum, neuronal activity influences oligodendrogenesis, which in turn facilitates adaptive myelination (Gibson et al., 2014).

Thus, rather than refuting the role of activity-dependent plasticity in nodal spacing, our findings emphasize the diversity of underlying regulatory mechanisms. We described these explicitly in text (page 10, line 18).

Significance

This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

This paper does not argue against node plasticity, but rather demonstrates that oligodendrocytes in the NL region exhibit a form of plasticity; they proliferate in response to vesicular release from NM axons, yet do not undergo morphological changes, ensuring adequate oligodendrocyte density for the full myelination of the auditory circuit. Thus, activity-dependent plasticity involving oligodendrocytes would contributes in various ways to each neural circuit, which is presumably attributed to the fact that myelination is driven by complex multicellular interactions between diverse axons and oligodendrocytes. Oligodendrocytes are known to exhibit heterogeneity in morphology, function, responsiveness, and gene profiles (Foerster et al., 2019; Sherafat et al., 2021; Osanai et al., 2022; Valihrach et al., 2022), but functional significance of this heterogeneity remains largely unclear. This paper also provides insight into how oligodendrocyte heterogeneity may contribute to the fine-tuning of neural circuit function, adding further value to our findings. Importantly, our study covers the wide range of development in the sound localization circuit, from the pre-myelination (E9) to the postfunctional maturation (P9), revealing how the nodal spacing pattern along the axon in this circuit emerges and matures.

Reviewer #2:

Evidence, reproducibility and clarity

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Major points, detailed below, need to be addressed to overcome some limitations of the study.

Major comments:

(1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

eTeNT is a widely used genetically encoded silencing tool and constructs similar to the one used in this study have been successfully applied in primates and rodents to suppress target behaviors via genetic dissection of specific pathways (Kinoshita et al., 2012; Sooksawate et al., 2013). However, precisely quantifying the extent of vesicular release inhibition from NM axons in the brainstem auditory circuit is technically problematic.

One major limitation is that while A3V efficiently infects NM neurons, its transduction efficiency does not reach 100%. In electrophysiological evaluations, NL neurons receive inputs from multiple NM axons, meaning that responses may still include input from uninfected axons. Additionally, failure to evoke synaptic responses could either indicate successful silencing or failure to stimulate NM axons, making a clear distinction difficult. Furthermore, unlike in motor circuits, we cannot assess the effect of silencing by observing behavioral outputs.

Thus, we instead opted to quantify the precise expression efficiency of GFP-tagged eTeNT in the cell bodies of NM neurons. The proportion of NM neurons expressing GFP-tagged eTeNT was 89.7 ± 1.6% (N = 6 chicks), which is consistent with previous reports evaluating A3V transduction efficiency in the brainstem auditory circuit (Matsui et al., 2012). These results strongly suggest that synaptic transmission from NM axons was globally silenced by eTeNT at the NL region. We described these explicitly in text (page 8, line 2).

(2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

Figure 6D depicts a representative axon selected from a dense population of GFP-positive axons in a 200-μm-thick slice after A3V-eTeNT infection to bilateral NM. As shown in Supplementary Video 1 and 2, densely labeled GFP-positive axons can be traced along the main trunk. To prevent any misinterpretation, we have revised the description of Figure 6 in the main text and Figure legend (page 31, line 9), and stated the A3V-eTeNT infection efficiency was 89.7 ± 1.6% in NM neurons, as mentioned above. Based on this efficiency, we interpreted that the global occlusion of vesicular release from most of the NM axons altered the pericellular microenvironment of the NL region, which led to the regional effect on the oligodendrocyte density.

On the other hand, your question regarding whether sparse expression of eTeNT still has an effect is highly relevant. As we also discussed in our reply to comment 4 by Reviewer #1, the relationship between neuronal activity and oligodendrocytes is highly diverse. In some types of axons, vesicular release is essential for normal myelination, and this process was disrupted by TeNT (Koudelka et al., 2016), suggesting that direct interaction with oligodendrocytes via vesicle release may actively promote myelination in these types of axons.

To clarify whether the phenotype observed in Figure 6 arises from changes in the pericellular microenvironment at the NL region or from the direct suppression of axon-oligodendrocyte interactions, we included a new Supplementary Figure (Figure 6—figure supplement 1). In this figure, we evaluated the node formation on the axon sparsely expressing eTeNT by electroporation into the unilateral NM. The results showed that sparse eTeNT expression did not increase the percentages of heminodes or unmyelinated segments. This finding supports our conclusion that the increased unmyelinated segments by A3V-eTeNT resulted from impaired synaptic transmission at NM terminals and subsequent alterations of  pericellular microenvironment at the NL region.

(3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

Thank you for your valuable suggestions to improve the rigor of our statistical analyses. We have reanalyzed all statistical tests using R software. In the revised Methods section and Figure Legends, we have clarified the rationale for selecting each statistical test, specified which test was used for each figure, and explicitly defined both n and N. After reevaluation with the Shapiro-Wilk test, we adjusted some analyses to non-parametric tests where appropriate. However, these adjustments did not alter the statistical significance of our results compared to the original analyses.

(3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or MannWhitney U test, which are much more common in the field. What is the rationale for the test choice?

We have revised the explanation of our statistical test choices to provide greater clarity and precision. For example, in Figure 2G, we first assessed the normality of the data in each of the four groups using the Shapiro-Wilk test, which revealed that some datasets did not follow a normal distribution. Given this, we selected the Kruskal-Wallis test, a commonly used non-parametric test for comparisons across three or more groups. Since the Kruskal-Wallis test indicated a significant difference, we conducted a post hoc Steel-Dwass test to determine which specific group comparisons were statistically significant.

(3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a ttest used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

In the case of Figures 3H-K, we compared oligodendrocyte morphology between regions. However, since the number of sparsely labeled oligodendrocytes differs both between regions and across individual samples, there is no strict correspondence between paired measurements. On the other hand, in Figures 5B, C, and E, we compared the density of labeled cells between regions within the same slice, establishing a direct correspondence between paired data points. For these comparisons, we appropriately used a paired t-test.

(3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

We have specified the statistical tests used for each figure in the Methods section and Figure Legends for better clarity. Additionally, we have revised the descriptions for Figure 4G, L, and M and their corresponding Figure Legends to explicitly indicate that Spearman’s rank correlation coefficient (rₛ) was used for evaluation.

(3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

We have now clarified what “n” represents in each figure, as well as the number of animals (N) used in each experiment, in the Figure Legends.

In this study, developmental stages of chick embryos were defined by HH stage (Hamburger and Hamilton, 1951), minimizing individual variability. Additionally, since our study focuses on the distribution of morphological characteristics of individual cells, averaging measurements per animal would obscure important cellular-level variability and potentially mislead interpretation of data. Furthermore, we employed a strategy of sparse genetic labeling in many experiments, which naturally results in variability in the number of measurable cells per animal. Given the clear distinctions in our data distributions, we believe that averaging per biological replicate is not essential in this case.

To further ensure the robustness of our statistical analysis, data presented as boxplots were preliminarily assessed using PlotsOfDifferences, a web-based application that calculates and visualizes effect sizes and 95% confidence intervals based on bootstrapping (https://huygens.science.uva.nl/PlotsOfDifferences/; https://doi.org/10.1101/578575). Effect sizes can serve as a valuable alternative to p-values (Ho, 2018; https://www.nature.com/articles/s41592019-0470-3). The significant differences reported in our study are also supported by clear differences in effect sizes, ensuring that our conclusions remain robust regardless of the statistical approach used.

If requested, we would be happy to provide PlotsOfDifferences outputs as supplementary source data files, similar to those used in eLife publications, for each figure.

(3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

We have now incorporated individual data points into the bar graphs in Figures 5 and 6.

(4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

The morphological differences of oligodendrocytes between white and gray matter are well established (i.e. shorter myelin at gray matter), but their correspondence with the nodal spacing pattern along the long axonal projections of cortical neurons is not well understood. Future research may find similarities with our findings. Additionally, as mentioned in the final section of the Discussion, the mammalian brainstem auditory circuit is functionally analogous to the avian ITD circuit. Regional differences in nodal spacing along axons have also been observed in the mammalian system, raising the important question of whether these differences are supported by regional heterogeneity in oligodendrocytes. Investigating this possibility will facilitate our understanding of the underlying logic and mechanisms for determining node spacing patterns along axons, as well as provide valuable insights into evolutionary convergence in auditory processing mechanisms. We described these explicitly in text (page 11, line 34).

(5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

As you summarized in your comment, our results demonstrated that A3V-eTeNT suppressed oligodendrogenesis in the NL region, leading to a reduction in oligodendrocyte density (Figures 6L, M), which caused the emergence of unmyelinated segments. While this is an indirect manipulation of oligodendrocyte density, it nonetheless provides evidence supporting a causal relationship between oligodendrocyte density and nodal spacing.

The emergence of unmyelinated segments at the NL region further suggests that the myelin extension capacity of oligodendrocytes differs between regions, highlighting regional differences in intrinsic properties of oligodendrocyte as the most prominent determinant of nodal spacing variation. However, as you correctly pointed out, our findings do not establish direct causation.

In the future, developing methods to artificially manipulate myelin length could provide a more definitive demonstration of causality. Given these considerations, we have modified the title to replace "determine" with "underlie", ensuring that our conclusions are presented with appropriate nuance.

(6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

(6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

(6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

(6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812).

(6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

Thank you for your insightful suggestions. We have incorporated the relevant references you provided and revised the manuscript accordingly to contextualize our findings within the existing literature.

Minor comments:

(7) Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.

Response: 

Thank you for your suggestion. We have corrected the units in Figure 1G to µm

(8) The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

Oligodendrocytes and OPCs have small cell bodies, often comparable in size to their nuclei. The central void in the ring-like Olig2 staining pattern appears too small to represent the nucleus. Additionally, a similar ring-like appearance is observed in BrdU labeling (Figure 5G), suggesting that this staining pattern may reflect nuclear morphology or other structural features.

Significance

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

The main finding of our study is that the primary determinant of the biased nodal spacing pattern in the sound localization circuit is the regional heterogeneity in the morphology of oligodendrocytes due to their intrinsic properties (e.g., their ability to produce and extend myelin sheaths) rather than the density of the cells. This was based on our observations that a reduction of oligodendrocyte density by A3V-eTeNT expression caused unmyelinated segments but did not increase internodal length (Figure 6), further revealing the importance of oligodendrocyte density in ensuring full myelination for the axons with short internodes. Thus, we think that our study could propose the significance of oligodendrocyte heterogeneity in the circuit function as well as in the nodal spacing using experimental manipulation of oligodendrocyte density. 

Reviewer #3:

Evidence, reproducibility and clarity

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing. I have some major concerns:

(1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

Thank you for your insightful comment regarding the potential role of pre-nodal clusters in determining internodal length. Indeed, studies in zebrafish have suggested that pre-nodal clustering of node components prior to myelination may prefigure internodal length (Vagionitis et al., 2022). We have incorporated a discussion on whether such pre-nodal clusters could contribute to regional differences in nodal spacing in our manuscript (page 9, line 35).

Whether pre-nodal clusters are detectable before myelination appears to depend on neuronal subpopulation (Freeman et al., 2015). To investigate the presence of pre-nodal clusters along NM axons in the brainstem auditory circuit, we previously attempted to visualize AnkG signals at E13 and E14. However, we did not observe clear structures indicative of pre-nodal clusters; instead, we only detected sparse fibrous AnkG signals with weak Nav clustering at their ends, consistent with hemi-node features. This result does not exclude the possibility of pre-nodal clusters on NM axons, as the detection limit of immunostaining cannot be ruled out. In brainstem slices, where axons are densely packed, nodal molecules are expressed at low levels across a wide area, leading to a high background signal in immunostaining, which may mask weak pre-nodal cluster signals prior to myelination. Regarding the comment on Figure 1D, we assume you are referring to Figure 2D based on the context. The lack of clarity in the high-magnification images in Figure 2D results from both the high background signal and the limited penetration of the MAG antibody. Furthermore, we are unable to verify Neurofascin accumulation at pre-nodal clusters, as there is currently no commercially available antibody suitable for use in chickens, despite our over 20 years of efforts to identify one for AIS research. Therefore, current methodologies pose significant challenges in visualizing pre-nodal clusters in our model. Future advancements, such as exogenous expression of fluorescently tagged Neurofascin at appropriate densities or knock-in tagging of endogenous molecules, may help overcome these limitations.

However, a key issue to be discussed in this study is not merely the presence or absence of prenodal clusters, but rather whether pre-nodal clusters—if present—would determine regional differences in internodal length. To address this possibility, we have added new data in Figure 6I, measuring the length of unmyelinated segments that emerged following A3V-eTeNT expression.

If pre-nodal clusters were fixed before myelination and predetermined internodal length, then the length of unmyelinated segments should be equal to or a multiple of the typical internodal length. However, our data showed that unmyelinated segments in the NL region were less than half the length of the typical NL internodal length, contradicting the hypothesis that fixed pre-nodal clusters determine internodal length along NM axons in this region.

(2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

As mentioned in our reply to comment 2 by Reviewer #1, the diameter of NM axons was already evaluated using electron microscopy (EM) in the pioneering study by Seidl et al., (2010). Additionally, EM-based analysis makes it difficult to clearly distinguish between the main trunk of NM axons and thin collateral branches at the NL region. Accordingly, we did not do the EM analysis in this revision. 

In Figure 4, we used palGFP, which is targeted to the cell membrane, allowing us to measure axon diameter by evaluating the distance between two membrane signal peaks. This approach minimizes the influence of the blurring of fluorescence signals on diameter measurements. Thus, we believe that our method is sufficient to evaluate the relative difference in axon diameters between regions and hence to show that axon diameter is not the primary determinant of the 3-fold difference in internodal length between regions. 

(3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

The heterogeneity in oligodendrocyte morphology would reflect differences in gene profiles, which, in turn, may arise from differences in their developmental origin and/or pericellular microenvironment of OPCs. We made this point as clear as possible in Discussion (page 9, line 21).

Significance

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

Theoretically, tenure protects good teachers, but is that really the case? I have had plenty of bad experiences with tenured teachers, and it now makes me wonder.

I recently learned about the ESSA in a different class and how it all relates to accountability. The ESSA requires schools to give students equal opportunity, and that ties into the SARC, which is a School Accountability Report Card that makes sure schools are being honest with the public about how the students are doing academically and that the public's tax dollars are being used effectively.

So in this scenario, how does the Code of Ethics come into play if a teacher is forced to intentionally go against their duty of informing students about varying points of view? Can they fight it by using the Code as a guide to what they should and should not be able to teach? How does that work?

I feel like it's not really outlining the expectations, though, because everyone thinks about ethics through a different perspective, so what one teacher thinks is the right thing might be the total opposite to another teacher, and I think that creates a slippery slope upon which all teacher behaviors are defined

Unfortunately no matter how hard educators try to promote ethical behavior. No matter how hard the parents of victims fight to protect their children, there seems to be this weed-like part of society that just perseveres through history and teaches hate 101 at home, and tutors their young on how to properly stay in the past and harm others in the process.

Ethical codes have a way of changing over the years. There was a moment in history when segregation was thought to be fair and ethical by many. In a couple years we will be appalled by the things we think are ethical today.

I wonder what kind of content the books were that led to them being banned.

This is a clear example of why students should be aware of their rights. I've had an experience where the whole class had to be searched for no real reason.

Exactly! Equitably! In sixth grade I moved to a new school and I had one of those teachers that pulled popsicle sticks and gave me anxiety. To this day i get nervous when the term tectonic comes up anywhere. I will never forget the day she kept pulling sticks until my name came out. later on IN HIGHSCHOOL I had an amazing dance teacher who gave me the confidence I needed through dance.

I think this is important for all students to be aware of and have the ability to protect themselves

Working with preschoolers, and from experience, it is inevitable for children not to get injured (minor injuries). It is unfair to label it as negligence. We all do our best to keep the children safe and prevent any injuries from happening, but at the end of the day, they are kids full of energy and curiosity.

No way! All children are different, they have different strengths and weaknesses, for the lack of a better word. The saying, "Everybody is a genius but if you judge a fish by its ability to climb a tree, it will live its whole live believing it is stupid". Some children dread what other students look forward to. It is their time to learn to maneuver their way socially academically heck even professionally. if the pressure is equal someone is bound to snap.

If teachers have to make decisions based on what they think is ethical, then if parents disagree and complain about said decisions, how does that situation get resolved if everything is based in a "gray area"? Who makes the final call and how?

I've been seeing a lot of discourse surrounding teacher outfits/dress code on social media lately, which is interesting because in the past, it has almost always been centered on students.

I believe teachers are there to teach, but to some extent they do perform the roles of parents by caring for the students. Teachers perform this role by preventing injuries and fostering strong trust relationships which leads to some students seeing their teachers as parental models while in school.

eLife Assessment

This important study combines electrocardiographic (ECG) and heart/torso anatomy data from subjects included in the UK Biobank to analyze sex-specific differences in relationships between those two characteristics. The study has several compelling strengths, including the development of an open-source pipeline for reconstruction and analysis of heart/torso geometry from a large cohort. Nevertheless, technical analysis of the data as presented is incomplete, specifically as it pertains to assessment of co-linearity between regressed parameters, interpretation of regression coefficients for sex and/or presence of myocardial infarction, and discussion of potential roles played by underlying electrophysiological derangements. With improvements to these aspects of the analysis, the paper would be of interest to the cardiovascular research community, especially those studying highly relevant health and treatment disparities arising from sex differences.

Reviewer #1 (Public review):

Summary:

The electrocardiogram (ECG) is routinely used to diagnose and assess cardiovascular risk. However, its interpretation can be complicated by sex-based and anatomical variations in heart and torso structure. To quantify these relationships, Dr. Smith and colleagues developed computational tools to automatically reconstruct 3D heart and torso anatomies from UK Biobank data. Their regression analysis identified key sex differences in anatomical parameters and their associations with ECG features, particularly post-myocardial infarction (MI). This work provides valuable quantitative insights into how sex and anatomy influence ECG metrics, potentially improving future ECG interpretation protocols by accounting for these factors.

Strengths:

(1) The study introduces an automated pipeline to reconstruct heart and torso anatomies from a large cohort (1,476 subjects, including healthy and post-MI individuals).

(2) The 3-stage reconstruction achieved high accuracy (validated via Dice coefficient and error distances).

(3) Extracted anatomical features enabled novel analyses of disease-dependent relationships between sex, anatomy, and ECG metrics.

(4) Open-source code for the pipeline and analyses enhances reproducibility.

Weaknesses:

(1) The linear regression approach, while useful, may not fully address collinearity among parameters (e.g., cardiac size, torso volume, heart position). Although left ventricular mass or cavity volume was selected to mitigate collinearity, other parameters (e.g., heart center coordinates) could still introduce bias.

(2) The study attributes residual ECG differences to sex/MI status after controlling for anatomical variables. However, regression model errors could distort these estimates. A rigorous evaluation of potential deviations (e.g., variance inflation factors or alternative methods like ridge regression) would strengthen the conclusions.

(3) The manuscript's highly quantitative presentation may hinder readability. Simplifying technical descriptions and improving figure clarity (e.g., separating superimposed bar plots in Figures 2-4) would aid comprehension.

(4) Given established sex differences in QTc intervals, applying the same analytical framework to explore QTc's dependence on sex and anatomy could have provided additional clinically relevant insights.

Reviewer #2 (Public review):

Summary:

Missed diagnosis of myocardial ischemia (MI) is more common in women, and treatment is typically less aggressive. This diagnosis stems from the fact that women's ECGs commonly exhibit 12 lead ECG biomarkers that are less likely to fall within the traditional diagnostic criteria. Namely, women have shorter QRS durations and lower ST junction and T wave amplitudes, but longer QT intervals, than men. To study the impact, this study aims to quantify sex differences in heart-torso anatomy and ECG biomarkers, as well as their relative associations, in both pre- and post-MI populations. A novel computational pipeline was constructed to generate torso-ventricular geometries from cardiac magnetic resonance imaging. The pipeline was used to build models for 425 post-myocardial infarction subjects and 1051 healthy controls from UK Biobank clinical images to generate the population.

Strengths:

This study has a strength in that it utilizes a large patient population from the UK Biobank (425 post-MI and 1051 healthy controls) to analyze sex-based differences. The computational pipeline is state-of-the-art for constructing torso-ventricular geometries from cardiac MR and is clinically viable. It draws on novel machine learning techniques for segmentation, contour extraction, and shape modeling. This pipeline is publicly available and can help in the large-scale generation of anatomies for other studies. This allows computation of various anatomical factors (torso volume, cavity volume, etc), and subsequent regression analysis on how these factors are altered before and after MI from the 12-lead ECG.

Weaknesses:

Major weaknesses stem from the fact that, while electrophysiological factors appear to play a role across many leads, both post-MI and healthy, the electrophysiological factors are not stated or discussed. The computational modeling pipeline is validated for reconstructing torso contours; however, potential registration errors stemming from ventricular-torso construction are not addressed within the context of anatomical factors, such as the tilt and rotation of the heart. This should be discussed as the paper's claims are based on these results. Further analysis and explanation are needed to understand how these sex-specific results impact the ECG-based diagnosis of MI in men and women, as stated as the primary reason for the study at the beginning of the paper. This would provide a broader impact within the clinical community. Claims about demographics do not appear to be supported within the main manuscript but are provided in the supplements. Reformatting the paper's structure is required to efficiently and effectively present and support the findings and outcomes of this work.

Angolul "Since there are no in-progress items in a deposit transaction, all movements are recorded in the Settled holdings" helyett inkább Since there are no items under settlement in deposit transactions, all movements are recorded in the Settled balance.

Flip that

Ship a capability and terms together ensure they work for ever! Like in the old days when you installed an app on your Personal Computer

Make everything ever green and future proof

Then people can decide if they want to use the new stuff with new terms

continuos upgrades and no guarantees of old versions working forever is another level of loss of data sovereignity

data sovereignity requires capability/application soverignity and future compatibility

71% of global technology decision makers see the value in establishing data sovereignty for their organisations.

Source: A commissioned study conducted Forrester Consulting on behalf of Element

that sounds really cool. AR seems like an amazing tool for the future of this field.

I did not know this was an opinion people had on archeology. All the people I know who learns of my interest in archeology often mentions the game Temple Run or Indiana Jones. Archeology as the catalyst to an adventure

This reminded me of a virtual reality exhibition called "L'horizon de Kheops" I went to at the Montreal Science Center. We wore VR headsets and walked through the building and went on a tour of ancient Egypt. Got to walk into and ontop of the pyramids while being guided by a virtual character. Would this count as digital archeology or something similar?

Quantify the impact of your instruction. Did it lead to fewer errors or higher quality projects? Provide metrics.

Include the impact of your training. Did it lead to improved safety records or student confidence?

Mention the user base or potential applications of this project. Who would benefit from it?

Detail what specific standards were created. How did they lead to the 40% cost reduction? Be more specific.

Quantify the impact of digitizing work. How much time or cost was saved? Include specific metrics.

Specify how you measured the improvement in understanding. Include metrics or feedback to enhance impact.

eLife Assessment

This valuable study investigates the self-assembly activity of death-fold domains. The data collected using advanced microscopy and distributed amphifluoric FRET-based flow cytometry methods provide solid evidence for the conclusions, although the interpretations based on these conclusions remain speculative in some cases. This paper is broad interest to those studying a variety of biological pathways involved in inflammatory responses and various forms of cell death.

Reviewer #1 (Public review):

Summary:

This is a high-quality and extensive study that reveals differences in the self-assembly properties of the full set of 109 human death fold domains (DFDs). Distributed amphifluoric FRET (DAmFRET) is a powerful tool that reveals the self-assembly behaviour of the DFDs, in non-seeded and seeded contexts, and allows comparison of the nature and extent of self-assembly. The nature of the barriers to nucleation is revealed in the transition from low to high AmFRET. Alongside analysis of the saturation concentration and protein concentration in the absence of seed, the subset of proteins that exhibited discontinuous transitions to higher-order assemblies was observed to have higher concentrations than DFDs that exhibited continuous transitions. The experiments probing the ~20% of DFDs that exhibit discontinuous transition to polymeric form suggest that they populate a metastable, supersaturated form in the absence of cognate signal. This is suggestive of a high intrinsic barrier to nucleation.

Strengths:

The differences in self-assembly behaviour are significant and likely identify mechanistic differences across this large family of signalling adapter domains. The work is of high quality, and the evidence for a range of behaviours is strong. This is an important and useful starting point since the different assembly mechanisms point towards specific cellular roles. However, understanding the molecular basis for these differences will require further analysis.

An impressive optogenetic approach was engineered and applied to initiate self-assembly of CASP1 and CASP9 DFDs, as a model for apoptosome initiation in these two DFDs with differing continuous or discontinuous assembly properties. This comparison revealed clear differences in the stability and reversibility of the assemblies, supporting the hypothesis that supersaturation-mediated DFD assembly underlies signal amplification in at least some of the DFDs.

The study reveals interesting correlations between supersaturation of DFD adapters in short- and long-lived cells, suggestive of a relationship between the mechanism of assembly and cellular context. Additionally, the comprehensive nature of the study provides strong evidence that the interactions are almost all homomeric or limited to members of the same DFD subfamily or interaction network. Similar approaches with bacterial proteins from innate immunity operons suggest that their polymerisation may be driven by similar mechanisms.

Weaknesses:

Only a limited investigation of assembly morphology was conducted by microscopy. There was a tendency for discontinuous structures to form fibrillar structures and continuous to populate diffuse or punctate structures, but there was overlap across all categories, which is not fully explored. The methodology used to probe oligomeric assembly and stability (SDD-AGE) does not justify the conclusions drawn regarding stability and native structure within the assemblies.

The work identifies important differences between DFDs and clearly different patterns of association. However, most of the detailed analysis is of the DFDs that exhibit a discontinuous transition, and important questions remain about the majority of other DFDs and why some assemblies should be reversible and others not, and about the nature of signalling arising from a continuous transition to polymeric form.

Some key examples of well-studied DFDs, such as MyD88 and RIPK,1 deserve more discussion, since they display somewhat surprising results. More detailed exploration of these candidates, where much is known about their structures and the nature of the assemblies from other work, could substantiate the conclusions here and transform some of the conclusions from speculative to convincing.

The study concludes with general statements about the relationship between stochastic nucleation and mortality, which provide food for thought and discussion but which, as they concede, are highly speculative. The analogies that are drawn with batteries and privatisation will likely not be clearly understood by all readers. The authors do not discuss limitations of the study or elaborate on further experiments that could interrogate the model.

Reviewer #2 (Public review):

Summary:

The manuscript from Rodriguez Gama et al. proposes several interesting conclusions based on different oligomerization properties of Death-Fold Domains (DFDs) in cells, their natural abundance, and supersaturation properties. These ideas are:<br /> (1) DFDs broadly store the cell's energy by remaining in a supersaturated state;<br /> (2) Cells are constantly in a vulnerable state that could lead to cell death;<br /> (3) The cell's lifespan depends on the supersaturation levels of certain DFDs.

Overall, the evidence supporting these claims is not completely solid. Some concerns were noted.

Strengths:

Systematic analysis of DFD self-assembly and its relationship with protein abundance, supersaturation, cell longevity, and evolution.

Weaknesses

(1) On page 2, it is stated, "Nucleation barriers increase with the entropic cost of assembly. Assemblies with large barriers, therefore, tend to be more ordered than those without. Ordered assembly often manifests as long filaments in cells," as a way to explain the observed results that DFDs assemblies that transitioned discontinuously form fibrils, whereas those that transitioned continuously (low-to-high) formed spherical or amorphous puncta. It is unlikely to be able to differentiate between amorphous and structured puncta by conventional confocal microscopy. Some DFDs self-assemble into structured puncta formed by intertwined fibrils. Such fibril nets are more structured and thus should be associated with a higher entropic cost. Therefore, the results in Figure 1B do not seem to agree with the reasoning described.

(2) Errors for the data shown in Figure 1B would have been very useful to determine whether the population differences between diffuse, punctate, and fibrillar for the continuous (low-to-high) transition are meaningful.

(3) A main concern in the data shown in Figure 1B and F is that the number of counts for discontinuous compared to continuous is small. Thus, the significance of the results is difficult to evaluate in the context of the broad function of DFDs as batteries, as stated at the beginning of the manuscript.

(4) The proteins or domains that are self-seeded (Figure 1F) should be listed such that the reader has a better understanding of whether domains or full-length proteins are considered, whether other domains have an effect on self-seeding (which is not discussed), and whether there is repetition.

(5) The authors indicate an anticorrelation between transcript abundance and Csat based on the data shown in Figure 2B; however, the data are scattered. It is not clear why an anticorrelation is inferred.

(6) It would be useful to indicate the expected range of degree centrality. The differences observed are very small. This is specifically the case for the BC values. The lack of context and the small differences cast doubts on their significance. It would be beneficial to describe these data in the context of the centrality values of other proteins.

(7) Page 3 section title: "Nucleation barriers are a characteristic feature of inflammatory signalosome adaptors." This title seems to contradict the results shown in Figure 2D, where full-length CARD9 and CARD11 are classified as sensors, but it has been reported that they are adaptor proteins with key roles in the inflammatory response. Please see the following references as examples: The adaptor protein CARD9 is essential for the activation of myeloid cells through ITAM-associated and Toll-like receptors. Nat Immunol 8, 619-629 (2007), and Mechanisms of Regulated and Dysregulated CARD11 Signaling in Adaptive Immunity and Disease. Front Immunol. 2018 Sep 19;9:2105.

However, both CARD9 and CARD11 show discontinuous to continuous behavior for the individual DFDs versus full-length proteins, respectively, in contrast to the results obtained for ASC, FADD, etc. FADD plays a key role in apoptosis but shows the same behavior as BCL10 and ASC. However, the manuscript indicates that this behavior is characteristic of inflammatory signalosomes. What is the explanation for adaptor proteins behaving in different ways? This casts doubts about the possibility of deriving general conclusions on the significance of these observations, or the subtitles in the results section seem to be oversimplifications.

(8) IFI16-PYD displays discontinuous behavior according to Figure S1H; however, it is not included in Figure 2D, but AIM 2 is.

(9) To demonstrate that "Nucleation barriers facilitate signal amplification in human cells," constructs using APAF1 CARD, NLRC4 CARD, caspase-9 CARD, and a chimera of the latter are used to create what the authors refer to as apoptsomes. Even though puncta are observed, referring to these assemblies as apoptosomes seems somewhat misleading. In addition, it is not clear why the activity of caspase-9 was not measured directly, instead of that of capsae-3 and 7, which could be activated by other means. The polymerization of caspase-1 CARD with NLRC4 CARD, leading to irreversible puncta, could just mean that the polymers are more stable. In fact, not all DFDs form equally stable or identical complexes, which does not necessarily imply that a nucleation barrier facilitates signal amplification. Could this conclusion be an overstatement?

(10) To demonstrate that "Innate immune adaptors are endogenously supersaturated," it is stated on page 5 that ASC clusters continue to grow for the full duration of the time course and that AIM2-PYD stops growing after 5 min. The data shown in Figure 4F indicate that AIM2-PYD grows after 5 mins, although slowly, and ASC starts to slow down at ~ 13 min. Because ASC has two DFDs, assemblies can grow faster and become bigger. How is this related to supersaturation?

Author response:

We appreciate constructive feedback from both reviewers. Reviewer 1 provided a very positive assessment and helpful suggestions for clarity, which we will incorporate.

We also thank Reviewer 2 for their detailed comments. In some instances, their public review raised concerns about specific data or interpretations that are, in fact, already presented and justified in the original manuscript. This feedback has highlighted a need to improve the clarity of our presentation. 

In our revised manuscript, we will make key information more prominent to prevent further misunderstandings. We will also provide additional statistical validation for our conclusions, additional data from the optogenetic experiments and high throughput imaging, and further elaborate on the behaviors of specific proteins (FADD, MyD88, and RIPK1). We are confident that these revisions will make our findings more transparent and accessible to readers, and we look forward to submitting our revised manuscript.

I like this idea of “removing the fence” because in art class, barriers often come from rigid materials or expectations. For example, a student with motor challenges may not be able to hold a paintbrush in the same way, but if I design a project that allows painting with sponges, digital tablets, or even finger-painting, then they can still fully participate. UDL reminds me to think about flexible entry points for creativity, not just giving one kind of “step stool.”

This resonates with me as an art teacher because digital access isn’t just about having the tool but knowing how to use it meaningfully. Many of my students use phones or Chromebooks to watch videos or play games, but far fewer know how to use free art platforms like Google Drawings or Pixlr to create. It makes me think about how I can model creative uses of tech so they don’t just stay passive consumers but become active digital artists.

This makes complete sense but is not something I would have thought of affecting this type of work

Would be interesting to know the arguments on both sides and how they define both of them. with my lack of knowledge on either it makes sense to me for them to be the same field?

edit: my question is mostly answered in a few lines - looks to be because of extra training and more ethical guidelines so I can see why it should be distinguished from "general" archaeology

A Lucene index is a core component of Apache Lucene, an open-source, full-text search engine library written in Java. It is designed to enable fast and efficient searching across large volumes of data. Here's a breakdown of what a Lucene index entails: Inverted Index: The fundamental structure of a Lucene index is an inverted index. Instead of mapping documents to the terms they contain (like a traditional database), an inverted index maps each unique term to a list of documents in which that term appears. This structure allows for very rapid retrieval of documents containing specific query terms. Documents and Fields: Lucene indexes are built from "documents," which are the basic units of indexing and searching. Each document is composed of one or more "fields," which are essentially named values representing different attributes of the document (e.g., "title," "content," "author"). Indexing Process: To create a Lucene index, data is processed and added to the index. This typically involves: Creating an IndexWriter: This object manages the process of adding, updating, and deleting documents from the index. Specifying Index Directory and Configuration: The location where the index files will be stored and various indexing parameters are defined. Adding Fields to Documents: Data from your source (e.g., a file, a database record) is transformed into Lucene documents with appropriate fields. These fields can be configured for various purposes, such as being indexed for search, stored for retrieval, or analyzed for full-text search. Segments: A Lucene index is not a single monolithic file but rather a collection of smaller, independent index structures called "segments." New documents are typically added to new segments, and over time, these segments are merged to optimize performance and reduce file count. Search Functionality: Once an index is built, Lucene provides APIs to perform queries on it. These queries can range from simple term searches to complex Boolean queries, phrase searches, and more advanced features like fuzzy matching and proximity searches. The results are typically ranked by relevance, though custom sorting criteria can also be applied.

This explains that creativity is very broad and many people can have it no matter what. It comes from imagination and thinking outside of the limits. I highlighted this section because it shows how the brain works.

This text adds onto the explanation of what Occam's Razor is. It is a way to cut assumptions that are not needed. It means to know when the simplest answer works and when it's more complicated than that.

Scientists like to test easier hypothese even if you can get more information using a more complex hypothesis.

(p. 265) "I knew right there in prison reading had changed forever the course of my life" Prison can be inspiring for some people and it's inspiring that he chose to look at prison so positively and gaining knowledge from it.

(p. 262) "The world's most monstrous crime, the sin and blood on the white man's hands, are almost impossible to believe." I really like this quote because slavery is so horrific, and I believe schools didn't teach enough about how awful it actually was.

(p. 258) "I saw that the best thing to do was get hold of a dictionary-- to study, to learn some words." I think it was very smart of Malcom to take advantage of the resources he did have.

The "Divine Comedy South" I found here on the Internet seems like Dante's The Divine Comedy, a famous long poem written by the Italian poet Dante Aligieri in the 14th century. He described the journey of the soul between hell and heaven (Alighieri, 1320/2003). Linney's title might be hinting at a satirical or symbolic journey to hell of Southern culture or figures. The connection between the two clearly shows that the communication between the two characters in the play is revealing the darkness of human nature.

isn't that the golden parachute

1,25-(OH)2-vitamin D (calcitriol) Consider using "calcitriol" and "calcidiol" throughout the discussion to make clear that calcidiol is the most abundant form in serum and calcitriol is the active form of Vit D. These terms make it easier to read than the others. Also, these are the terms used in Fig 3.

calcitriol, the

glucagon. Directions to carry the glucagon kit with them at all times should be given. Patient compliance in this regard is variable to non-adherent so must be encouraged repeatedly.

corrected. Furthermore, once blood glucose level decreases during management of DKA, glucose may be administered along with insulin to avoid hypoglycemia as the patient is rehydrated and the electrolyte balance is returned to normal.

ketone bodies. ("ketones" and "ketone bodies" are inexact names, but as they are used in the literature and clinic, we should probably keep using these terms. However, for the written word, it is better to say "ketone bodies" than just "ketones." Not all ketone bodies are actual ketones (acetone and acetoacetate are ketones, beta-hydroxybutyrate is not a ketone).

The increase in two of the ketone bodies, acetoacetate and beta-hydroxybutyrate, leads to acidemia.

by the rise in ATP after

ability of the pancreas

This is a repeat of a sentence used at the beginning of the DM section. Delete.

the insulin receptor,

metanephrines (including the clinical analytes metanephrine and normetanephrine, derived from epinephrine and norepinephrine, respectively).

If designers want to create effective mobile UIs, they need to know the differences each app has, its features, and the way users interact with them. There are no longer any standards in relation to the type of screens that work for each app because the mobile industry is evolving so quickly. More importantly, designers must be flexible, and adaptable, and modify the way they think about mobile design based upon not only emerging technologies, but also user expectations, which means continuous improvement and modifications based upon numerous iterations, revisions, and learning from user experience. In this light, much of the mobile experience will also consist of an evergreen state of learning from users, and designers will need to be open to changing their approach.

I think what the author is trying to convey by using the term " galaxie francophone" is to do a metaphor stating that in that galaxy of French speakers, 255 million people live in some other type of world or planet.

This graph is expressing the evolution of language speakers through the years, it shows francophones being at around 200 million in 1965 and eventually growing. I think it also refers to a prediction of how many francophones there will be in following years.

I think the article will definitely be informative. The title itself is giving us a question in where the article will respond to it by giving us information and statistics about it. The article tells us many informative things such as how many countries around the world speak French, and estimations of how many French speakers there is.

Cognate for the English word "territories".

Langue in French is a cognate for the English word " Language"

Chat GPT isn't just helpful if used the right way it can help researchers write quicker and how they think about their own skills. This could even encourge any writing habits for improvement.

From what I understood of the theory is about how people see themselves on who they want to be, and how they feel about that difference such as self image and to find out who they wanna be and even with their self esteem

It seems like the studies are trying to prove to us that AI tools affect researchers feelings about themselves, and technology shapes their behavior even in the future.

In this paragraph, instead of looking at plagiarism or anything related to that, the study is relating to people and how ai influences people to think about themselves as real researchers.

ChatGPT makes writing easier and more of a cleaner look, especially for people who aren’t native English speakers. But it also makes people worry and start thinking to themselves like “Is this really my work?” or “Am I cheating?” It can be helpful, but also very stressful to one.

Esta combinación me resulta muy útil porque articula distintos niveles de metadatos: metas, emociones, reflexiones y tareas. En archivística y bibliotecología hablamos de “contexto” y “contenido”; aquí el journaling los integra en un mismo registro.

Me parece curiosa esta parte y es que muestra cómo el diario intersticial convierte hasta lo más simple como es la hora, una tarea o una idea en algo organizado. Sin imaginar, terminas con un registro completo de tu día sin tener que hacer demasiado y eso es lo que buscamos la mayoría algo practico.

Esta práctica me parece cercana a los diarios de investigación o a las bitácoras de campo. Permite capturar micro-evidencias de lo que ocurre en los intersticios del trabajo académico, fortaleciendo la trazabilidad del proceso de aprendizaje.

Como estudiante de Ciencia de la Información, me llama la atención cómo esta técnica une tres funciones centrales: registro, organización y control de tiempos. Esto recuerda a los sistemas integrados de gestión documental, donde cada acción debe quedar trazada y relacionada.

Escribir tus descansos cambia un momento perdido en un recordatorio para seguir enfocado en tus proyectos o metas.

Lo que me gusta de la idea del diario es que mezcla varias cosas a la vez como por ejemplo lo que planeas hacer, como te sientes, si has avanzado o aun no, y lo mas importante lo que queda pendiente. Me parece una forma sencilla de volver más conscientes los descansos.

Me parece maravilloso, es como ir dejando pequeñas huellas de tu día ya que apuntas lo que haces, cuándo lo haces y cómo te sientes. Eso ayuda a ver en qué inviertes tu tiempo, tambien te ayuda a retomar el foco rápido y a tener un registro honesto de tu jornada sin complicarte.

He leído sobre el diario intersticial y me llama mucho la atención. Me gusta que sea una forma sencilla de unir notas, tareas y tiempo en un solo flujo. Todavía no lo he probado, pero creo que podría ser una herramienta muy útil para organizarme mejor y darme más claridad en el día a día.

Es interesante la intensión del autor que es permitirnos auto pensarnos y revisar que cosas mejorar en como obligarnos a detenernos y registrar qué estamos haciendo y hacia dónde vamos. Podría funcionar como un a terapia contra la falta de concentración que se dan por las redes sociales. También las pausas para reflexionar, aparte de esforzar la memoria, permite aprender mas allá de lo común, también se ve como autorregula el aprendizaje autónomo esto requiere disciplina pero los resultados pueden ser buenos, para conocernos y optimizar todo mejor, pensarlo como una bitácora de detalles es interesante por que nos permite auto conocernos y autocriticarnos para mejorar, eso ayuda a conocernos como aprendemos mejor y puede optimizar nuestro foco de atenciones para poder concentrarse mejor.

Yo intente lo mismo con mi calendario donde incorpore alarmas y notas pero no funcionó mucho, me funciono mas llevar una agenda y con pos-it ir llevando notas de tareas, en este mundo trajinado es bueno tener herramientas que nos ayuden a conocernos y a como aprendemos mejor y quedarnos con esa opción para sentirnos realizados y que podemos servir mejor a nuestra familia y sociedad.

This is an example of viewpoint placement because the quote is inferring that the author also believes that pushing against immigration in Australia is "standing up for the country." This is bias by omission because it seems to really only take the side of the One Nation organization

This makes an assumption instead of showing fact that the government is avoiding an honest discussion. Though it is referencing One Nation, it ends the article on One Nation's opinion of the government's intentions.

The picture below this highlight shows photo bias of Australians smiling and adorned with Australian flags. This photo is pulling on the feelings of national pride that most Australians probably feel, and giving a feeling that to be properly Australian is to support the One Nation policies.

Though the article quotes Prime Minister Albanese who does not agree with the One Nation immigration policy, It is followed up with a quote that just gives number of international migration students. This give more information or expand on the quote, "Diversity is a strength in modern Australia." If anything, the quote minimizes that PM's statement because it gives a big number of people coming to Australia without expounding on what they provide the country. It then goes on to discuss problems with immigration like housing/services pressures, and uses word choice like insisted to point out positive parts to immigration. This makes the PM look desparate and with no real data or information.

This is both word choice and ommission.

The picture shows photo bias of Australians smiling and adorned with Australian flags. This photo is pulling on the feelings of national pride that most Australians probably feel, and giving a feeling that to be properly Australian is to support the One Nation policies.

Calling the break in voting patterns "a rebellion" is spin where the word sounds negative because it infers a level of chaos and treatery.

Is this saying that their academic identity changes over time -- going back to being Multifaceted do their identities change based on the perspective of a situation they are looking at ??

*** (The Multifaceted Ideas)

Yes using AI for non-native English speakers can be useful but it also starts raising questions about the authenticy behind your work. While using AI people should always be careful of maintaining their "academic identity." Take Grammarly for example, yes its helps plenty with grammar and punctuation.. But there are also so many cases where they will reccommend basically rewriting/rewording an entire sentence. And in my opinion that takes away the personal aspect of the writing

I think this is trying to say that AI can be explored in a connotation but we are so caught up in trying to detect the usage of AI that we arent using it to its full potential (and how it can benefit us)

ChatGPT can help "level the playing field" for non-native english speakers to write better when trying to share their work publicly - however I believe this will only put their opportunity to fully learn and engage with the writing themselves. Having it translated from Chat would take away the authenticity behind their work and wont translate the same?

save face FOR WHO

this goes along with the part in the declaration that states Britain only has power if they have the consent to rule.The colonists had already been exercising this finding before they wrote the declaration.

The thought of being taxed for something they worked hard must have frustrated them, as Locke had recently stated that education is important. Therefore, the British are putting taxes on key parts of their life (like education and a diploma).

Why would they demand them to go to war with the native americans when the debt is already high? Do they know that the king's attempt at lowering the debt is by using them yet?

At least there was an effort made to establish some equality. How can they victimize themselves for being invisible and disrespected, when there were multiple moments surrounding their grievances? I can see it getting to be tiring, but wouldn't the thought of one of the debates going well be enough of a promise to keep the patriots content?

Goes onto the idea that we are still trying to embody the values we set for ourselves hundreds of years ago. More specifically: the declaration of independence and the part where it preaches that "all men are created equal"

Final annotation - one question that came to mind when reading this article - Will developers end up giving new AI personality and voice to speak to members/users of ChatGPT? and what else do developers of AI plan to program AI for?

Big picture annotation - The main point of the article is to explain the development from the original Chat GPT editions to the most recent versions by explaining how the programed a training system for the AI to become accustomed to answering questions, and detecting what questions to answer. Also another thing this article was trying to tell us is the potential new future versions of AI and how different it would be from our present versions.

connection annotation - This sections connects to the video about ChatGPT because when the man briefly explained Tokens and how it took a big part in development, and how tokens helped with word sequences, and adapting to English.

Connection annotation - This section connects to the video during the middle-beginning when the man explained how the differences between the different versions of the ChatGPT.

restatement annotation - What this section is saying is that most words in English have two meaning or other ways of saying, so the "tokens" used to program AI helped with this problem.

Restatement annotation- In this section it pretty much states that the training codes encoded with ChatGPT allowed the AI to comprehend more words than a 10 year old kid.

Tracking annotation- In this section I am expecting to learn how the first model of ChatGPT shocked the world, and somewhat covering what was said in the video.

Tracking annotation - for the first tracking annotation i expect to learn how AI was developed to understand language, because English has a lot of words with different context.

Video annotation (summary)- AI was the fastest growing platform among TikTok and other social media platforms. ChatGPT was programed with something called tokens, tokens are a numerical representation of words said in the video, and was programed to analyze these words into sequences. ChatGPT was also programed to spot illegal requests from members/users of ChatGPT, dictating weather it should take answers from members/users that have valuable requests rather then negative requests. A question that came to mind, is that if ChatGPT is so complex like this, does the world plan on inventing other AI like this for another purpose?

Not only did they do better during the personalized problems, but they started to do better on normal word problems after the learning experiment as well.

I find this extremely interesting and actually relatable too. I tend to understand and retain information much more efficiently, as well as stay more focused on the learning material (instead of zoning out) when the information is being applied to topics I'm already interested in or have knowledge about.

I think this is a really great point to bring up and a great way to connect to the audience he's speaking to. In my experience at least, I know that I tend to overanalyze a lot of my work and knowing that someone else does that too makes me feel more at ease.

I really like this part of the speech because it is just so true. We could all physically experience the same thing but everybody would come out of that experience having a different interpretation of that event. I think how we interpret our experiences can say a lot about an individual and it is something I've always enjoyed hearing about.

they will get to you eventually be patient provide the correct information and it will be easier for them to find you.

this is extra help when writing a email to the professor so it makes it easier for them to know who you are.

This quote gives me the idea of human identity not as "poshuman" beings, but as part of Earth's cycles of decay and renewal. This emphasizes the connection to microbes, soil, and death. I think this view is unsettling because it give sus to face mortality while an ongoing ecological process.

Haraway worries that the extinction and the collapse are real. They stated that this highlights the seriousness of today's crises, and this is powerful because it provides a reason for what is happening to animals, humans, and ecosystems, which is a life-threatening situation that is not meant for entertainment.

Before tv was a one way conversation with the viewers. Now with social media ads are very accessible and easy to comment on.

Flow wasn't successful as other broadcasters tried to repiicate it. It wasn't the same.

Using that energy ended up hurting our natural environment.

This is when collective learning started to change the way human society worked.

Farming brought a new level to collective learning.

using stories and this type of learning, we survived all those years.

Modern Cosmotology states the universe does have limits, contary to the ancient Cosmotology which says the universe is infinite in size and time.

How did we come to the conclusion that we were not at the center? We finally realized we weren't at the center of the universe, which changes the way we think about history because of context.

Highlights how ads are part of our culture and the truth is that they've made a huge cultural impact

Lack on connection doesn't make it necessarily bad, I'm just curious of what the ads are trying to get across to the views.I believe that it is important to ask the question "why"

They don't relate and the ads are random, further proves what william said about lack of correlation in ads. I can understand how this is confusing and how this can make a viewer forget what theyre watching. I wonder if lack of ads in streaming helps viewers focus more on the shows.

A critical analysis that I personally agree with. I think its important to note that theres not connections but maybe thats so the channels can get a wide variety of viewers and not just stay in a box.