The fact the spainards take women as slaves shows their cruelty and how contradictory they are to being "civilized"

A detailed potryal of the awfulness of both sides of the atlantic slave trade where tribal leaders sold slaves to white europeans

Could be a double meaning for converting the slaves to christianity?

Does the setting also make this american literature then (takes place in south america)

Then does this not kind of invalidate the whole concept of slavery and racism the spainards participate in?

The identity of the american is a lot less descriptive then the spainard, possible eluding to the concept of the melting pot

Same ethnicity as past writers like De la Casas

could this mean the captian or the actual ship itself?

who?

outing the foreshadowing?

wow the imagery here with this simile is very intriguing

a lot of detail about this ship man.....

oh.

Kind of hospitality like

guidance

Wondering if he's still referencing the boat or an actual figure of a woman

perception

unfamiliar boat? Unrecognizable sailor? random ship coming into the bay

Test

Hippocampus atrophy → physiological inability to dampen the disease

don't need to be capitalist entrepreneur to put forth "capitalist spirit"

Economic enterprise doesn't equal capitalism

mindset of capitalism cannot be perpetuated by certain economic conditions- higher or lower wages- must be a long re-education on a mass scale

capitalism has biggest beef with traditionalism- which Weber will specify

there's always been randoms who only want to make money

ethical duty to do job in a capitalist society

money making as expression of virtue

closely related to religious ideas

Weird paradox- the making of money is the end goal as opposed to what can be produced from said money

values are beyond egocentric

Western capitalism distinct in its ethical coloring and purpose of daily life

because the aspects of modern life would not fare with the old protestants- we can't look for a sort of materialistic or non materialistic way of living

why are those who rejected wordliness also wealthy?

protestants are maybe now seen as indulgent albeit hardworking but that wasn't always the case- used to have very sober character

So now we have to look at the difference in religions

usually minority groups are the ones driving peculiar economic activities but not the case with the Catholics

beyond just a class or wealth issues- Protestants more likely to acquire skilled labour and administrative positions in factories than Catholics

Stratifications in inherited wealth from Protestants and Catholics can't account for the differences in education

misconception that protestant reformation brought about this separation of church and state that allowed for capitalist entrepreneurs to flourish but historically, that isn't the case

What do we want to know? Why are the business leaders, owners of capital, and more technically trained people all protestant?

find where legal system and formal perfection of West came from

what social structures fed into this use of technical knowledge in the west?

West had technical utilization of scientific knowledge

Concerned with development of western bourgeois class and what makes it unique, related but different from capitalistic organization of labor,

rational organization of capitalist enterprise result of: 1. separation of business from household 2. rational book-keeping

capitalistic economic action- exchange with the expectation of profit

The queerness of color is important because it can explain the difference between our worlds.

Purpose: This part of the podcast as well as the whole podcast is mainly entertainment with sprinkles of information when it comes to the success of Bad bunny. Though they are providing facts and data the nature of the podcast is for the viewers to find entertainment in talking about music artists and their journey.

Audio: They used background music to set the atmosphere for the views for the rest of the podcast. The music gives the viewers excited to listed to the conversation of this topic which is music. At the end of the podcast they play a Bad Bunny song which gives the fans of this artist some enjoyment in listening to the song and shows others who don't know who he is a taste of his music.

UTF-8

RPC and security. Kerberos V5 will get used.

At long last she has maybe reached her finally hope of getting out of that bird suit. This conveys that sometimes you have to accept the fate whether it turns out good or bad.

forcces them to kill yourself

blind to the danger, almost as if a sense of fear eludes them.

Margaret Atwood is referencing the song of a siren and how it is so irresistible it led sailors to their death.

Personally, I do not like such targeted advertisements, because it is a violation of privacy to let advertisers know too much about users' privacy. Advertisers are using social media to collect our information and make profits for themselves. I think the advertising push on social media can only refer to the age and gender information in each user's profile.

This is very true, and I believe almost all people who use social media can experience it. A social media will detect users' reactions when watching different types of videos, such as whether a video has been played completely, or if they swiped away at the moment they saw it; or whether they frequently liked certain types of works; even the frequency of leaving comments in others' posts. Social media will push the content that different users are most interested in based on the collected information. To be more specific, if I like food, then social media may often push some videos about food reviews and cooking tutorials to me. This mechanism has two sides. On the one hand, people can see the content they like more frequently, and on the other hand, this mechanism also limits the information that everyone sees.

the entire piece, specifically this sentence, is an example of the overall theme of the piece, which is also the repeated refrain of "clearly expressing something" and the consideration of the content expressed. The structure of this sentence presents concrete ideas but in a difficult-to-digest form (long run-on sentence, basic/filler words repeated often) Is the content about clearly expressing something being clearly expressed? if someone is clearly expressing something in a way that is not clear, does that take away the something of the thing expressed?

the humanity/the struggle makes him more human, more convincingly great

I have not been here; I would like to see these murals.

Value of knowledge in a zettelkasten as a function of reference(use/look up) frequency; links to other ideas; ease of recall without needing to look up (also a measure of usefulness); others?

Define terms and create a mathematical equation of stocks and flows around this system of information. Maybe "knowledge complexity" or "information optimization"? see: https://hypothes.is/a/zejn0oscEe-zsjMPhgjL_Q

takes into account the value of information from the perspective of a particular observer<br /> relative information value

cross-reference: Umberto Eco on no piece of information is superior: https://hypothes.is/a/jqug2tNlEeyg2JfEczmepw

Inspired by idea in https://hypothes.is/a/CdoMUJCYEe-VlxtqIFX4qA

inspired by, but definitely not take from as not in evidence

Many people have "daily notes" and "project notes" in what they consider to be their zettelkasten workflow. These can be thought of as subcategories of reference notes (aka literature notes, bibliographic notes). The references in these cases are simply different sorts of material than one would traditionally include in this category. Instead of indexing the ideas within a book or journal article, you're indexing what happened to you on a particular day (daily notes) or indexing ideas or progress on a particular project (project notes). Because they're different enough in type and form, you might keep them in their own "departments" (aka folders) within your system just the same way that with enough material one might break out their reference notes to separate books from newspapers, journal articles, or lectures.

In general form and function they're all broadly serving the same functionality and acting as a ratchet and pawl on the information that is being collected. They capture context; they serve as reminder. The fact that some may be used less or referred to less frequently doesn't make them necessarily less important

This could be one reason that the robot in Robot & Frank was faceless, and why most robots in current development that I have seen do not have a animal-like or human-like face.

This building of genuine connections and relationships is likely going to be the most difficult to emulate using robots. Perhaps human care could be more focused on this aspect of caregiving if robots were to take care of the menial aspects.

Even though they do very demanding work, caregivers are not usually compensated enough for the work they do. Removing this could be one of the main benefits of using technology to assist or replace this work.

I think social media affects relationship maintenance in some positive and some negative ways; positive in that partners are able to maintain intimacy over longer distances or time periods and communication is made easier, but negative in that the expectation of communication can make expectations of a relationship difficult. I know in my relationship, we had to manage our expectations around the other’s texting habits because it was upsetting both of us at the frequency of communication, but only because the world had made constant availability the norm.

It’s interesting that this preference is getting more and more distinct, rather than less pronounced. In a progressive society that is trying to reduce the amount of gender stereotypes that are displayed in our society, this one doesn’t seem to be one that’s very challenged. I know for myself, I definitely didn’t want to be approaching people when I was romantically engaging with new others, and my now-boyfriend is the one that reached out to me first. I don’t think female-initiating dances would work anymore either, as it’s clear that society dictates that men should be pursuing women. Why hasn’t this standard gone away when so many others have?

This speed dating experiment is really interesting, as I’ve heard of all of these studies that show that men value attractiveness and women value economic status, and that that supports an evolutionary hypothesis. It’s intriguing that a more ecologically valid approach shows us that these findings aren’t necessarily true, and that men and women aren’t very different in what we desire in a partner when in a real situation.

Sounds like you're doing what Mortimer J. Adler and Charles van Doren would call "inspectional reading" and outlining the space of your topic. This is both fine and expected. You have to start somewhere. You're scaffolding some basic information in a new space and that's worthwhile. You're learning the basics.

Eventually you may come back and do a more analytical read and/or cross reference your first sources with other sources in a syntopical read. It's at these later two levels of reading where doing zettelkasten work is much more profitable, particularly for discerning differences, creating new insights, and expanding knowledge.

If you want to think of it this way, what would a kindergartner's zettelkasten contain? a high school senior? a Ph.D. researcher? 30 year seasoned academic researcher? Are the levels of knowledge all the same? Is the kindergartner material really useful to the high school senior? Probably not at all, it's very basic. As a result, putting in hundreds of atomic notes as you're scaffolding your early learning can be counter-productive. Read some things, highlight them, annotate them. You'll have lots of fleeting notes, but most of them will seem stupidly basic after a month or two. What you really want as main notes are the truly interesting advanced stuff. When you're entering a new area, certainly index ideas, but don't stress about capturing absolutely everything until you have a better understanding of what's going on. Then bring your zettelkasten in to leverage yourself up to the next level.

• Adler, Mortimer J. “How to Mark a Book.” Saturday Review of Literature, July 6, 1940. https://www.unz.com/print/SaturdayRev-1940jul06-00011/
• Adler, Mortimer J., and Charles Van Doren. How to Read a Book: The Classical Guide to Intelligent Reading. Revised and Updated edition. 1940. Reprint, Touchstone, 2011.

reply to u/jack_hanson_c at https://old.reddit.com/r/Zettelkasten/comments/1g9dv9b/is_scoping_the_subject_a_counterzettelkasten/

thesis restated!

Thesis?

Thesis?

I wasn't really sure how the encryption process worked so this was interesting to read. The number of times I've forgotten my password at work and had to call for assistance the fact that there was a special line devoted just for that tells you how important it is to control this information and what it locks away.

I've often wondered what hackers could and could not do and in a lot of ways I'm sure there's no limitation to what havoc they can cause. A few companies I've worked for were very strict about opening emails that only came from the company itself. Reading all of the ways hackers can work around things makes you feel really vulnerable.

this adds code tags which could prevent content from being displayed for some Safari users (this is the case when the tags are wrapped around H5P activities)

replaced screenshot

redid the screenshot (without text)

Finally some serious attention to Native American interests and rights. But the overriding concern here seems to be enabling these groups, as well, to step up extraction from public lands.

Pendley's proposals would effectively dismantle decades of agency efforts to implement Congressional mandates by executive fiat, without consulting Congress. Challenges to many of these proposals are likely to succeed in court.

Pretty much all manual typewriters use 1/2" (12.7mm) wide ribbon which is most of what you're probably going to find in the marketplace.

The thing that changes from machine to machine is is the potentially proprietary spools and those are usually specific to how the ribbion auto switch is effectuated. Most ribbon comes with small grommets about 10-12 inches from the ends as many machines need this to trigger the switch over. If the plastic spools you purchase don't work with your particular machine you simply spend a minute or two to hand wind it onto your existing original (metal) spools and go from there.

There are lots of videos on YouTube showing how to hand wind ribbon onto a machine. Sarah has a pretty reasonable one: https://www.youtube.com/watch?v=up412FjTEkw

Even Tom Hanks has a ribbon changing video... https://www.youtube.com/watch?v=GBbsNKaVAB0

Incidentally, if your seller specifies them, the Underwoods take Group 9 (GR9) spools. Likely not helpful or illustrative for you, but certainly interesting from a historical perspective, Ted Munk has a catalog of Typewriter Ribbon varieties Offered by Underwood in 1956: https://munk.org/typecast/2020/08/23/typewriter-ribbon-varieties-offered-by-underwood-in-1956/

reply to u/prettiestGOAT at https://old.reddit.com/r/typewriters/comments/1g8z0fm/can_anyone_help_id_this_underwood_typewriter_and/

very abstract imagery, very intuitively written. Reminds me of poetry I've written that made sense to me but I've scrapped because it would be gibberish to others, but it's interesting to be a reader and to try and unpack the speaker's garbled thoughts.

Chris M. recommends to use a layered system for music categorization:

• Layer 1) Genres / Subgenres
• Layer 2) Energy
• Layer 3) Vibe

Genre itself is the main overall (and broad) genre. Subgenres are tag-like and related to when you want to play it more granularly.

Energy is a measurement of the average energy of the song.

Vibes refer to the emotions and memories it brings up to you and potentially others you play it for. Some questions he asks: - 1) How does it make me feel? - 2) What does it remind me of? - 3) Where would I play it? - 4) When would I play it? - 5) Why would I play it? - 6) Who would I play it for?

When looking for songs in the library, it's very important to answer a few questions to filter. Not just to save storage space, but also to ensure the quality of one's library.

Chris M. recommends a SHORT LIST... Music you come across that you like and think about downloading, you put in there. Then wait for 24h before listening again to it. Finally, ask 3 questions before deciding to add it: - 1) Do I still like it? - 2) Would I play it out? - 3) Would I pay money for it?

It reflects the multiplicity of the role that teachers play not only in the classroom, but also in the personal and social life of students. This focus on student equality and respect ensures that everyone feels included in the class and, hence, creates a climate of confidence and self-esteem. Standards in core subjects are essential, but so too is a commitment to life skills like the appreciation of diversity and respect. These lessons run far beyond the school gates and nurture children to be well-intentioned and compassionate citizens.

p7?

p6

p5 (in reference to p4)

p4 (CONT)

p4

p3?

Chris M. suggests to start building a playlist with the end in mind. This is logical because it's easier to backtrack transitions then to do it forward.

Edit: This he suggested in a different video, not this one.

Playing into the idea of transitions... Perhaps it's useful to keep small playlists with only a handful of songs (3-5) that are PERFECT together. This can be used as a sort of repository for the creation of larger playlists later to save time.

Transitions he mentions: - A) Instant RAGE Slower Song -> Instant Drop Instant Drop = a song that immediately pulls up the speed. - B) Slow Down, Speed Up Hype song with a gradual slow down, leading into an immediate speed up. Kick or beat drop from track 2. - C) Vibez to Vibez Track 1 to Track 2 while remaining energy (energy the same) but switching the genres. - D) Get up and Dance Weird ending that you CAN'T dance to leading into something that you HAVE to dance to. - E) The SLOW DOWN Song pace doesn't matter. Slow Ending to a Slow Beginning. - F) The Lit Switch Lead one LIT song seamlessly into another LIT song, regardless of genre. It maintains hype level. - G) Is that the same X Have a similar sounding X playing at the end of track 1 and at the beginning of track 2... X can be anything, for example an instrument/guitar. - H) Weird BUT Effective Track 2 starts with a sound effect and you use that to transition seamlessly with track 1. - I) The Dip/The Rip Track A with decent pace to Track B (slowest song of the 3) to Track C which starts slow but picks up the pace again Can be reversed into the hip... Then the middle song is the fastest song.

The distinguisher between a great playlist and a normal playlist is the flow between each songs. In other words, the transitions.

Excellent video on the creation of playlists.

This basically stated that America had exclusive power of Latin American countries

The Boxer Rebellion was a Chinese revolt against outward interaction with foreign nations. America smothered the flame of uprising because they wanted cheap trade with China.

the U.S. "dealt the final blow"

Wilson attacked Veracruz without the permission of congress

Trade of loans for authority over Latin America

Gave America the right to aquire lands with guano deposits

Mahan believed in overseas conquest which impacted Roosevelt's motivation to intervene in foreign places

Author response:

Reviewer #1 (Public Review):

Summary

The authors asked if parabrachial CGRP neurons were only necessary for a threat alarm to promote freezing or were necessary for a threat alarm to promote a wider range of defensive behaviors, most prominently flight.

Major Strengths of Methods and Results

The authors performed careful single-unit recording and applied rigorous methodologies to optogenetically tag CGRP neurons within the PBN. Careful analyses show that single-units and the wider CGRP neuron population increases firing to a range of unconditioned stimuli. The optogenetic stimulation of experiment 2 was comparatively simpler but achieved its aim of determining the consequence of activating CGRP neurons in the absence of other stimuli. Experiment 3 used a very clever behavioral approach to reveal a setting in which both cue-evoked freezing and flight could be observed. This was done by having the unconditioned stimulus be a "robot" traveling along a circular path at a given speed. Subsequent cue presentation elicited mild flight in controls and optogenetic activation of CGRP neurons significantly boosted this flight response. This demonstrated for the first time that CGRP neuron activation does more than promote freezing. The authors conclude by demonstrating that bidirectional modulation of CGRP neuron activity bidirectionally aTects freezing in a traditional fear conditioning setting and aTects both freezing and flight in a setting in which the robot served as the unconditioned stimulus. Altogether, this is a very strong set of experiments that greatly expand the role of parabrachial CGRP neurons in threat alarm.

We would like to sincerely thank the reviewer for the positive and insightful comments on our work. We greatly appreciate the acknowledgment of our new behavioral approach, which allowed us to observe a dynamic spectrum of defensive behaviors in animals. Our use of the robot-based paradigm, which enables the observation of both freezing and flight, has been instrumental in expanding our understanding of how parabrachial CGRP neurons modulate diverse threat responses. We are pleased that the reviewer found this methodological innovation to be a valuable contribution to the field.

Weaknesses

In all of their conditioning studies the authors did not include a control cue. For example, a sound presented the same number of times but unrelated to US (shock or robot) presentation. This does not detract from their behavioral findings. However, it means the authors do not know if the observed behavior is a consequence of pairing. Or is a behavior that would be observed to any cue played in the setting? This is particularly important for the experiments using the robot US.

We appreciate the reviewer’s insightful comment regarding the absence of a control cue in our conditioning studies. First, we would like to mention that, in response to the Reviewer 3, we have updated how we present our flight data by following methods from previously published papers (Fadok et al., 2017; Borkar et al., 2024). Instead of counting flight responses, we calculated flight scores as the ratio of the velocity during the CS to the average velocity in the 7 s before the CS on the conditioning day (or 10 s for the retention test). This method better captures both the speed and duration of fleeing during CS. With this updated approach, we observed a significant difference in flight scores between the ChR2 and control groups, even during conditioning, which may partly address the reviewer’s concern about whether the observed behavior is a consequence of CS-US pairing.

However, we agree with the reviewer that including an unpaired group would provide stronger evidence, and in response, we conducted an additional experiment with an unpaired group. In this unpaired group, the CS was presented the same number of times, but the robot US was delivered randomly within the inter-trial interval. The unpaired group did not exhibit any notable conditioned freezing or flight responses. We believe that this additional experiment, now reflected in Figure 3, further strengthens our conclusion that the fleeing behavior is driven by associative learning between the CS and US, rather than a reaction to the cue itself.

The authors make claims about the contribution of CGRP neurons to freezing and fleeing behavior, however, all of the optogenetic manipulations are centered on the US presentation period. Presently, the experiments show a role for these neurons in processing aversive outcomes but show little role for these neurons in cue responding or behavior organizing. Claims of contributions to behavior should be substantiated by manipulations targeting the cue period.

We appreciate the reviewer’s constructive comments. We would like to emphasize that our primary objective in this study was to investigate whether activating parabrachial CGRP neurons—thereby increasing the general alarm signal—would elicit different defensive behaviors beyond passive freezing. To this end, we focused on manipulating CGRP neurons during the US period rather than the cue period.

Previous studies have shown that CGRP neurons relay US signals, and direct activation of CGRP neurons has been used as the US to successfully induce conditioned freezing responses to the CS during retention tests (Han et al., 2015; Bowen et al., 2020). In our experiments, we also observed that CGRP neurons responded exclusively to the US during conditioning with the robot (Figure 1F), and stimulating these neurons in the absence of any external stimuli elicited strong freezing responses (Figure 2B). These findings, collectively, suggest that activation of CGRP neurons during the CS period would predominantly result in freezing behavior.

Therefore, we manipulated the activity of CGRP neurons during the US period to examine whether adjusting the perceived threat level through these neurons would result in diverse dfensive behaivors when paired with chasing robot. We observed that enhancing CGRP neuron activity while animals were chased by the robot at 70 cm/s made them react as if chased at a higher speed (90 cm/s), leading to increased fleeing behaviors. While this may not fully address the role of these neurons in cue responding or behavior organizing, we found that silencing CGRP neurons with tetanus toxin (TetTox) abolished fleeing behavior even when animals were chased at high speeds (90 cm/s), which usually elicits fleeing without CGRP manipulation (Figure 5). This supports the conclusion that CGRP neurons are necessary for processing fleeing responses.

In summary, manipulating CGRP neurons during the US period was essential for effectively investigating their role in adjusting defensive responses, thereby expanding our understanding of their function within the general alarm system. We hope this clarifies our experimental design and addresses the concern the reviewer has raised.

Appraisal

The authors achieved their aims and have revealed a much greater role for parabrachial CGRP neurons in threat alarm.

Discussion

Understanding neural circuits for threat requires us (as a field) to examine diverse threat settings and behavioral outcomes. A commendable and rigorous aspect of this manuscript was the authors decision to use a new behavioral paradigm and measure multiple behavioral outcomes. Indeed, this manuscript would not have been nearly as impactful had they not done that. This novel behavior was combined with excellent recording and optogenetic manipulations - a standard the field should aspire to. Studies like this are the only way that we as a field will map complete neural circuits for threat.

We sincerely thank the reviewer for their positive and encouraging comments. We are grateful for the acknowledgment of our efforts in employing a novel behavioral paradigm to study diverse defensive behaviors. We are pleased that our work contributes to advancing the understanding of neural circuits involved in threat responses.

Reviewer #3 (Public Review):

Strengths:

The study used optogenetics together with in vivo electrophysiology to monitor CGRP neuron activity in response to various aversive stimuli including robot chasing to determine whether they encode noxious stimuli diTerentially. The study used an interesting conditioning paradigm to investigate the role of CGRP neurons in the PBN in both freezing and flight behaviors.

Weakness:

The major weakness of this study is that the chasing robot threat conditioning model elicits weak unconditioned and conditioned flight responses, making it diTicult to interpret the robustness of the findings. Furthermore, the conclusion that the CGRP neurons are capable of inducing flight is not substantiated by the data. No manipulations are made to influence the flight behavior of the mouse. Instead, the manipulations are designed to alter the intensity of the unconditioned stimulus.

We sincerely thank the reviewer for the thoughtful and constructive comments on our manuscript. In response to this feedback, we revisited our analysis of the flight responses and compared our methods with those used in previous literatures examining similar behaviors.

We reviewed a study investigating sex differences in defensive behavior using rats (Gruene et al., 2015). In that study, the CS was presented for 30 s, and active defensive behvaior – referred to as ‘darting’ – was quantified as ‘Dart rate (dart/min)’. This was calculated by doubling the number of darts counted during the 30-s CS presentation to extrapolate to a per-min rate. The highest average dart rate observed was approximatley 1.5. Another relevant studies using mice quantified active defensive behavior by calculating a flight score—the ratio of the average speed during each CS to the average speed during the 10 s pre-CS period (Fadok et al., 2017; Borkar et al., 2024). This method captures multiple aspects of flight behavior during CS presentation, including overall velocity, number of bouts, and duration of fleeing. Moreover, it accounts for each animal’s individual velocity prior to the CS, reflecting how fast the animals were fleeing relative to their baseline activity.

In our original analysis, we quantified flight responses by counting rapid fleeing movements, defined as movements exceeding 8 cm/s. This approach was consistent with our previous study using the same robot paradigm to observe unique patterns of defensive behavior related to sex differences (Pyeon et al., 2023). Based on our earlier findings, where this approach effectively identified significant differences in defensive behaviors, we believed that this method was appropriate for capturing conditioned flight behavior within our specific experimental context. However, prompted by the reviewer's insightful comments, we recognized that our initial method might not fully capture the robustness of the flight responses. Therefore, we re-analyzed our data using the flight score method described by Fadok and colleagues, which provides a more sensitive measure of fleeing during the CS.

Re-analyzing our data revealed a more robust flight response than previously reported, demonstrating that additional CGRP neuron stimulation promoted flight behavior in animals during conditioning, addressing the concern that the data did not substantiate the role of CGRP neurons in inducing flight. In addition, we would like to emphasize the findings from our final experiment, where silencing CGRP neurons, even under high-threat conditions (90 cm/s), prevented animals from exhibiting flight responses. This demonstrates that CGRP neurons are necessary in influencing flight responses.

We have updated all flight data in the manuscript and revised the relevant figures and text accordingly. We appreciate the opportunity to enhance our analysis. The reviewer's insightful observation led us to adopt a better method for quantifying flight behavior, which substantiates our conclusion about the role of CGRP neurons in modulating defensive responses.

Borkar, C.D., Stelly, C.E., Fu, X., Dorofeikova, M., Le, Q.-S.E., Vutukuri, R., et al. (2024). Top- down control of flight by a non-canonical cortico-amygdala pathway. Nature 625(7996), 743-749.

Bowen, A.J., Chen, J.Y., Huang, Y.W., Baertsch, N.A., Park, S., and Palmiter, R.D. (2020). Dissociable control of unconditioned responses and associative fear learning by parabrachial CGRP neurons. Elife 9, e59799.

Fadok, J.P., Krabbe, S., Markovic, M., Courtin, J., Xu, C., Massi, L., et al. (2017). A competitive inhibitory circuit for selection of active and passive fear responses. Nature 542(7639), 96-100.

Gruene, T.M., Flick, K., Stefano, A., Shea, S.D., and Shansky, R.M. (2015). Sexually divergent expression of active and passive conditioned fear responses in rats. Elife 4, e11352.

Han, S., Soleiman, M.T., Soden, M.E., Zweifel, L.S., and Palmiter, R.D. (2015). Elucidating an a_ective pain circuit that creates a threat memory. Cell 162(2), 363-374.

Pyeon, G.H., Lee, J., Jo, Y.S., and Choi, J.-S. (2023). Conditioned flight response in female rats to naturalistic threat is estrous-cycle dependent. Scientific Reports 13(1), 20988.

Knower / observer not separate from the universe, not outside the system boundary Vgl [[Systems convening landscape als macroscope 20230906115130]] where the convener is integral part of it too, not an external change agent.

we don't allow any additional software. security software is just one example. please clarify

please remove this bullet point.. we decided to only limit it to Red Hat compatibility list. VMware for example is on it, so VMware is okay for us

why did you remove the fakeraid statement I had?

Apa?

apa?

eLife Assessment

So et al. present an optimized protocol for single-nuclei RNA sequencing of adipose tissue in mice, ensuring better RNA quality and nuclei integrity. The authors use this protocol to explore the cellular landscape in both lean and diet-induced obese mice, identifying a dysfunctional hypertrophic adipocyte subpopulation linked to obesity. The data analyses are solid, and the findings are supported by the evidence presented. This study provides valuable information for the field of adipose tissue biology and will be particularly helpful for researchers using single-nuclei transcriptomics in various tissues.

Reviewer #1 (Public review):

Summary:

This manuscript from So et al. describes what is suggested to be an improved protocol for single-nuclei RNA sequencing (snRNA-seq) of adipose tissue. The authors provide evidence that modifications to the existing protocols result in better RNA quality and nuclei integrity than previously observed, with ultimately greater coverage of the transcriptome upon sequencing. Using the modified protocol, the authors compare the cellular landscape of murine inguinal and perigonadal white adipose tissue (WAT) depots harvested from animals fed a standard chow diet (lean mice) or those fed a high-fat diet (mice with obesity).

Strengths:

Overall, the manuscript is well written, and the data are clearly presented. The strengths of the manuscript rest in the description of an improved protocol for snRNA-seq analysis. This should be valuable for the growing number of investigators in the field of adipose tissue biology that are utilizing snRNA-seq technology, as well as those other fields attempting similar experiments with tissues possessing high levels of RNAse activity.

Moreover, the study makes some notable observations that provide the foundation for future investigation. One observation is the correlation between nuclei size and cell size, allowing for the transcriptomes of relatively hypertrophic adipocytes in perigonadal WAT to be examined. Another notable observation is the identification of an adipocyte subcluster (Ad6) that appears "stressed" or dysfunctional and likely localizes to crown-like inflammatory structures where pro-inflammatory immune cells reside.

Weaknesses:

Analogous studies have been reported in the literature, including a notable study from Savari et al. (Cell Metabolism). This somewhat diminishes the novelty of some of the biological findings presented here. This is deemed a minor criticism as the primary goal is to provide a resource for the field.

Reviewer #2 (Public review):

Summary:

In the present manuscript So et al describe an optimized method for nuclei isolation and single nucleus RNA sequencing (snRNA-Seq), which they use to characterize cell populations in lean and obese murine adipose tissues.

Strengths:

The detailed description of the protocol for single-nuclei isolation incorporating VRC may be useful to researchers studying adipose tissues, which contain high levels of RNAses.

While the majority of the findings largely confirm previous published adipose data sets, the authors present a detailed description of a mature adipocyte sub-cluster that appears to represent stressed or dying adipocytes present in obesity, and which is better characterized using the described protocol.

Weaknesses:

The use of VRC to enhance snRNA-seq has been previously published in other tissues, somewhat diminishing the novelty of this protocol.

The snRNA-seq data sets presented in this manuscript, when compared with numerous previously published single-cell analysis of adipose tissue, represent an incremental contribution. The nuclei-isolation protocol may represent an improvement in transcriptional analysis for mature adipocytes, however other stromal populations may be better sequenced using single intact-cell cytoplasmic RNA-Seq.

Reviewer #3 (Public review):

The authors aimed to improve single-nucleus RNA sequencing (snRNA-seq) to address current limitations and challenges with nuclei and RNA isolation quality. They successfully developed a protocol that enhances RNA preservation and yields high-quality snRNA-seq data from multiple tissues, including a challenging model of adipose tissue. They then applied this method to eWAT and iWAT from mice fed either a normal or high-fat diet, exploring depot-specific cellular dynamics and gene expression changes during obesity. Their analysis included subclustering of SVF cells and revealed that obesity promotes a transition in APCs from an early to a committed state and induces a pro-inflammatory phenotype in immune cells, particularly in eWAT. In addition to SVF cells, they discovered six adipocyte subpopulations characterized by a gradient of unique gene expression signatures. Interestingly, a novel subpopulation, termed Ad6, comprised stressed and dying adipocytes with reduced transcriptional activity, primarily found in eWAT of mice on a high-fat diet. Overall, the methodology is sound, and the data presented supports the conclusions drawn. Further research based on these findings could pave the way for potential novel interventions in obesity and metabolic disorders, or for similar studies in other tissues or conditions.

Strengths:

The authors have presented a compelling set of results. They have compared their data with two previously published datasets and provide novel insight into the biological processes underlying mouse adipose tissue remodeling during obesity. The results are generally consistent and robust. The revised Discussion is comprehensive and puts the work in the context of the field.

Weaknesses:

• The adipose tissues were collected after 10 weeks of high-fat diet treatment, lacking the intermediate time points for identifying early markers or cell populations during the transition from healthy to pathological adipose tissue.<br /> • The expansion of the Ad6 subpopulation in obese iWAT and gWAT is interesting. The author claims that Ad6 exhibited a substantial increase in eWAT and a moderate rise in iWAT (Figure 4C). However, this adipocyte subpopulation remains the most altered in iWAT upon obesity. Could the authors elaborate on why there is a scarcity of adipocytes with ROS reporter and B2M in obese iWAT?<br /> • While the study provides extensive data on mouse models, the potential translation of these findings to human obesity remains uncertain.

Revised version: The authors have properly revised the paper in response to the above questions, and I have no other concerns.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This manuscript from So et al. describes what is suggested to be an improved protocol for single-nuclei RNA sequencing (snRNA-seq) of adipose tissue. The authors provide evidence that modifications to the existing protocols result in better RNA quality and nuclei integrity than previously observed, with ultimately greater coverage of the transcriptome upon sequencing. Using the modified protocol, the authors compare the cellular landscape of murine inguinal and perigonadal white adipose tissue (WAT) depots harvested from animals fed a standard chow diet (lean mice) or those fed a high-fat diet (mice with obesity). 

Strengths: 

Overall, the manuscript is well-written, and the data are clearly presented. The strengths of the manuscript rest in the description of an improved protocol for snRNA-seq analysis. This should be valuable for the growing number of investigators in the field of adipose tissue biology that are utilizing snRNA-seq technology, as well as those other fields attempting similar experiments with tissues possessing high levels of RNAse activity. 

Moreover, the study makes some notable observations that provide the foundation for future investigation. One observation is the correlation between nuclei size and cell size, allowing for the transcriptomes of relatively hypertrophic adipocytes in perigonadal WAT to be examined. Another notable observation is the identification of an adipocyte subcluster (Ad6) that appears "stressed" or dysfunctional and likely localizes to crown-like inflammatory structures where proinflammatory immune cells reside. 

Weaknesses:  

Analogous studies have been reported in the literature, including a notable study from Savari et al. (Cell Metabolism). This somewhat diminishes the novelty of some of the biological findings presented here. Moreover, a direct comparison of the transcriptomic data derived from the new vs. existing protocols (i.e. fully executed side by side) was not presented. As such, the true benefit of the protocol modifications cannot be fully understood. 

We agree with the reviewer’s comment on the limitations of our study. Following the reviewer's suggestion, we performed a new analysis by integrating our data with those from the study by Emont et al. Please refer to the Recommendation for authors section below for further details.

Reviewer #2 (Public Review):

Summary: 

In the present manuscript So et al utilize single-nucleus RNA sequencing to characterize cell populations in lean and obese adipose tissues. 

Strengths: 

The authors utilize a modified nuclear isolation protocol incorporating VRC that results in higherquality sequencing reads compared with previous studies. 

Weaknesses:  

The use of VRC to enhance snRNA-seq has been previously published in other tissues. The snRNA-seq snRNA-seq data sets presented in this manuscript, when compared with numerous previously published single-cell analyses of adipose tissue, do not represent a significant scientific advance. 

Figure 1-3: The snRNA-seq data obtained by the authors using their enhanced protocol does not represent a significant improvement in cell profiling for the majority of the highlighted cell types including APCs, macrophages, and lymphocytes. These cell populations have been extensively characterized by cytoplasmic scRNA-seq which can achieve sufficient sequencing depth, and thus this study does not contribute meaningful additional insight into these cell types. The authors note an increase in the number of rare endothelial cell types recovered, however this is not translated into any kind of functional analysis of these populations. 

We acknowledge the reviewer's comments on the limitations of our study, particularly the lack of extension of our snRNA-seq data into functional studies of new biological processes. However, this manuscript has been submitted as a Tools and Resources article. As an article of this type, we provide detailed information on our snRNA-seq methods and present a valuable resource of high-quality mouse adipose tissue snRNA-seq data. In addition, we demonstrate that our improved method offers novel biological insights, including the identification of subpopulations of adipocytes categorized by size and functionality. We believe this study offers powerful tools and significant value to the research community.

Figure 4: The authors did not provide any evidence that the relative fluorescent brightness of GFP and mCherry is a direct measure of the nuclear size, and the nuclear size is only a moderate correlation with the cell size. Thus sorting the nuclei based on GFP/mCherry brightness is not a great proxy for adipocyte diameter. Furthermore, no meaningful insights are provided about the functional significance of the reported transcriptional differences between small and large adipocyte nuclei. 

To address the reviewer's point, we analyzed the Pearson correlation coefficient for nucleus size vs. adipocyte size and found R = 0.85, indicating a strong positive correlation. In addition, we performed a new experiment to determine the correlation between nuclear GFP intensity and adipocyte nucleus size, finding a strong correlation with R = 0.91. These results suggest that nuclear GFP intensity can be a strong proxy for adipocyte size. Furthermore, we performed gene ontology analysis on genes differentially regulated between large and small adipocyte nuclei. We found that large adipocytes promote processes involved in insulin response, vascularization and DNA repair, while inhibiting processes related to cell migration, metabolism and the cytoskeleton. We have added these new data as Figure 4E, S6E, S6G, and S6H (page 11)

Figure 5-6: The Ad6 population is highly transcriptionally analogous to the mAd3 population from Emont et al, and is thus not a novel finding. Furthermore, in the present data set, the authors conclude that Ad6 are likely stressed/dying hypertrophic adipocytes with a global loss of gene expression, which is a well-documented finding in eWAT > iWAT, for which the snRNA-seq reported in the present manuscript does not provide any novel scientific insight. 

As the reviewer pointed out, a new analysis integrating our data with the previous study found that Ad3 from our study is comparable to mAd3 from Emont et al. in gene expression profiles. However, significant discrepancies in population size and changes in response to obesity were observed, likely due to differences in technical robustness. The dysfunctional cellular state of this population, with compromised RNA content, may have hindered accurate capture in the previous study, while our protocol enabled precise detection. This underscores the importance of our improved snRNA-seq protocol for accurately understanding adipocyte population dynamics. We have revised the manuscript to include new data in Figure S7 (page 14).

Reviewer #3 (Public Review): 

Summary:  

The authors aimed to improve single-nucleus RNA sequencing (snRNA-seq) to address current limitations and challenges with nuclei and RNA isolation quality. They successfully developed a protocol that enhances RNA preservation and yields high-quality snRNA-seq data from multiple tissues, including a challenging model of adipose tissue. They then applied this method to eWAT and iWAT from mice fed either a normal or high-fat diet, exploring depot-specific cellular dynamics and gene expression changes during obesity. Their analysis included subclustering of SVF cells and revealed that obesity promotes a transition in APCs from an early to a committed state and induces a pro-inflammatory phenotype in immune cells, particularly in eWAT. In addition to SVF cells, they discovered six adipocyte subpopulations characterized by a gradient of unique gene expression signatures. Interestingly, a novel subpopulation, termed Ad6, comprised stressed and dying adipocytes with reduced transcriptional activity, primarily found in eWAT of mice on a high-fat diet. Overall, the methodology is sound, the writing is clear, and the conclusions drawn are supported by the data presented. Further research based on these findings could pave the way for potential novel interventions in obesity and metabolic disorders, or for similar studies in other tissues or conditions. 

Strengths:  

• The authors developed a robust snRNA-seq technique that preserves the integrity of the nucleus and RNA across various tissue types, overcoming the challenges of existing methods. 

• They identified adipocyte subpopulations that follow adaptive or pathological trajectories during obesity. 

• The study reveals depot-specific differences in adipose tissues, which could have implications for targeted therapies. 

Weaknesses: 

• The adipose tissues were collected after 10 weeks of high-fat diet treatment, lacking the intermediate time points for identifying early markers or cell populations during the transition from healthy to pathological adipose tissue. 

We agree with the reviewers regarding the limitations of our study. To address the reviewer’s comment, we revised the manuscript to include this in the Discussion section (page 17).  

• The expansion of the Ad6 subpopulation in obese iWAT and gWAT is interesting. The author claims that Ad6 exhibited a substantial increase in eWAT and a moderate rise in iWAT (Figure 4C). However, this adipocyte subpopulation remains the most altered in iWAT upon obesity. Could the authors elaborate on why there is a scarcity of adipocytes with ROS reporter and B2M in obese iWAT?

We observed an increase in the levels of H2DCFA reporter and B2M protein fluorescence in adipocytes from iWAT of HFD-fed mice, although this increase was much less compared to eWAT, as shown in Figure 6B (left panel). These increases in iWAT were not sufficient for most cells to exceed the cutoff values used to determine H2DCFA and B2M positivity in adipocytes during quantitative analysis. We have revised the manuscript to clarify these results (page 13).

• While the study provides extensive data on mouse models, the potential translation of these findings to human obesity remains uncertain. 

To address the reviewer’s point, we expanded our discussion on the differences in adipocyte heterogeneity between mice and humans. We attempted to identify human adipocyte subclusters that resemble the metabolically unhealthy Ad6 adipocytes found in mice in our study; however, we did not find any similar adipocyte types. It has been reported that human adipocyte heterogeneity does not correspond well to that of mouse adipocytes (Emont et al. 2022). In addition, the heterogeneity of human adipocyte populations is not reproducible between different studies (Massier et al. 2023). Interestingly, this inconsistency is unique to adipocytes, as other cell types in adipose tissues display reproducible sub cell types across species and studies (Massier et al. 2023). Our findings indicate that adipocytes may exhibit a unique pathological cellular state with significantly reduced RNA content, which may contribute to the poor consistency in adipocyte heterogeneity in prior studies with suboptimal RNA quality. Therefore, using a robust method to effectively preserve RNA quality may be critical for accurately characterizing adipocyte populations, especially in disease states. It may be important to test in future studies whether our snRNA-seq protocol can identify consistent heterogeneity in adipocyte populations across different species, studies, and individual human subjects. We have revised the manuscript to include this new discussion (page 17).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Suggested points to address: 

(1) The authors suggest that their improved protocol for maintaining RNA/nucleus integrity results in a more comprehensive analysis of adipose tissue heterogeneity. The authors compare the quality of their snRNA-seq data to those generated in prior studies (e.g., Savari et al.). What is not clear is whether additional heterogeneity/clusters can be observed due directly to the protocol modifications. A direct head-to-head comparison of the protocols executed in parallel would of course be ideal; however, integrating their new dataset with the corresponding data from Savari et al. could help address this question and help readers understand the benefits of this new protocol vs. existing protocols. 

The data from Savari et al. are of significantly lower quality, likely because they were generated using earlier versions of the 10X Genomics system, and this study lacks iWAT data. To address the reviewer’s point, we instead integrated our data with those from the other study by Emont et al. (2022), which used comparable tissue types and experimental systems. The integrated analysis confirmed the improved representation of all cell types present in adipose tissues in our study, with higher quality metrics such as increased Unique Molecular Identifiers (UMIs) and the number of genes per nucleus. These results indicate that our protocol offers significant advantages in generating a more accurate representation of each cell type and their gene expression profiles. New data are included in Figure S2 (page 7).

(2) The exact frequency of the Ad6 population in eWAT of mice maintained on HFD is a little unclear. From the snRNA-seq data, it appears that roughly 47% of the adipocytes are in this "stressed state." In Figure 6, it appears that greater than 75% of the adipocytes express B2M (Ad6 marker) and greater than 75% of adipocytes are suggested to be devoid of measurable PPARg expression. The latter seems quite high as PPARg expression is essential to maintain the adipocyte phenotype. Is there evidence of de-differentiation amongst them (i.e. acquisition of progenitor cell markers)? Presenting separate UMAPs for the chow vs. HFD state may help visualize the frequency of each adipocyte population in the two states. Inclusion of the stromal/progenitor cells in the visualization may help understand if cells are de-differentiating in obesity as previously postulated by the authors. Related to Point # 1 above, is this population observed in prior studies and at a similar frequency?

To address the reviewer’s point, we analyzed the expression of adipocyte progenitor cell (APC) markers, such as Pdgfra, in the Ad6 population. We did not detect significant expression of APC markers, suggesting that Ad6 does not represent dedifferentiating adipocytes. Instead, they are likely stressed and dying cells characterized by an aberrant state of transcription with a global decline.

When integrating our data with the datasets by Emont et al., we observed an adipocyte population in the previous study, mAd3, comparable to Ad6 in our study, with similar marker gene expression and lower transcript abundance. However, the population size of mAd3 was much smaller than that of Ad6 in our data and did not show consistent population changes during obesity. This discrepancy may be due to different technical robustness; the dysfunctional cellular state of this population, with its severely compromised RNA contents, may have made it difficult to accurately capture using standard protocols in the previous study, while our protocol enabled robust and precise detection. We added new data in Figure S6I and S7 (page 14) and revised the Discussion (page 17).

Additional points  

(1) The authors should be cautious in describing subpopulations as "increasing" or "decreasing" in obesity as the data are presented as proportions of a parent population. A given cell population may be "relatively increased." 

To address the reviewer's point, we revised the manuscript to clarify the "relative" changes in cell populations during obesity in the relevant sections (pages 8, 9, 10, 11, and 15).

(2) The authors should also be cautious in ascribing "function" to adipocyte populations based solely on their expression signatures. Statements such as those in the abstract, "...providing novel insights into the mechanisms orchestrating adipose tissue remodeling during obesity..." should probably be toned down as no such mechanism is truly demonstrated. 

To address the reviewer's point, we revised the manuscript by removing or replacing the indicated terms or phrases with more suitable wording in the appropriate sections (page 2, 10, 12, 14)

Reviewer #3 (Recommendations For The Authors): 

(1) The authors might consider expanding a discussion on the potential implications of their findings, especially the newly identified adipocyte subpopulations and depot-specific differences for human studies. 

To address the reviewer’s point, we attempted to identify human adipocyte subclusters that resembled our dysfunctional Ad6 adipocytes in mice; however, we did not find any similar adipocyte types. It has been reported that human adipocyte heterogeneity does not correspond well to that of mouse adipocytes (Emont et al. 2022). In addition, the heterogeneity of human adipocyte populations is not reproducible between different studies (Massier et al. 2023). Interestingly, this inconsistency is unique to adipocytes, as other cell types in adipose tissues display reproducible sub cell types across species and studies (Massier et al. 2023). Our findings indicate that adipocytes may exhibit a unique pathological cellular state with significantly reduced RNA content, which may contribute to the poor consistency in adipocyte heterogeneity in prior studies with suboptimal RNA quality. Therefore, using a robust method to effectively preserve RNA quality may be critical for accurately characterizing adipocyte populations, especially in disease states. It may be important to test in future studies whether our snRNA-seq protocol can identify consistent heterogeneity in adipocyte populations across different species, studies, and individual human subjects. We have revised the manuscript to include this new discussion (page 17)

(2) typo: "To generate diet-induced obesity models". 

We revised the manuscript to correct it.

How does the practice of self-disclosure evolve over time in relationships, and what impact does this evolution have on the overall emotional intimacy and trust between partners?

I chose to highlight this part because it emphasizes the foundational role that rituals play in fostering stability and connection within relationships. By creating predictability, rituals can enhance relationship quality and intimacy, making them essential tools for couples to navigate both everyday life and significant transitions, such as remarriage. Understanding this can encourage couples to establish and maintain meaningful rituals that strengthen their bond.

I chose to highlight this part because it underscores the critical role that relational uncertainty plays in creating a chaotic relationship environment. Understanding how uncertainty contributes to relational turbulence can help individuals recognize and address potential issues in their relationships, fostering healthier communication and stability.

Author response:

Reviewer #1 (Public Review):

The authors examined the hypothesis that plasma ApoM, which carries sphingosine-1-phosphate (S1P) and activates vascular S1P receptors to inhibit vascular leakage, is modulated by SGLT2 inhibitors (SGLTi) during endotoxemia. They also propose that this mechanism is mediated by SGLTi regulation of LRP2/ megalin in the kidney and that this mechanism is critical for endotoxin-induced vascular leak and myocardial dysfunction. The hypothesis is novel and potentially exciting. However, the author's experiments lack critical controls, lack rigor in multiple aspects, and overall does not support the conclusions.

Thank you for these comments. We have now directly addressed this hypothesis by using proximal tubule-specific inducible megalin/Lrp2 knockout mice, which remains an innovative hypothesis about how SGLT2i can reduce vascular leak.

Reviewer #2 (Public Review):

Apolipoprotein M (ApoM) is a plasma carrier for the vascular protective lipid mediator sphingosine 1-phospate (S1P). The plasma levels of S1P and its chaperones ApoM and albumin rapidly decline in patients with severe sepsis, but the mechanisms for such reductions and their consequences for cardiovascular health remain elusive. In this study, Ripoll and colleagues demonstrate that the sodium-glucose co-transporter inhibitor dapagliflozin (Dapa) can preserve serum ApoM levels as well as cardiac function after LPS treatment of mice with diet-induced obesity. They further provide data to suggest that Dapa preserves serum ApoM by increasing megalin-mediated reabsorption of ApoM in renal proximal tubules and that ApoM improves vascular integrity in LPS treated mice. These observations put forward a potential therapeutic approach to sustain vascular protective S1P signaling that could be relevant to other conditions of systemic inflammation where plasma levels of S1P decrease. However, although the authors are careful with their statements, the study falls short of directly implicating megalin in ApoM reabsorption and of ApoM/S1P depletion in LPS-induced cardiac dysfunction and the protective effects of Dapa.

The observations reported in this study are exciting and potentially of broad interest. The paper is well written and concise, and the statements made are mostly supported by the data presented. However, the mechanism proposed and implied is mostly based on circumstantial evidence, and the paper could be substantially improved by directly addressing the role of megalin in ApoM reabsorption and serum ApoM and S1P levels and the importance of ApoM for the preservation for cardiac function during endotoxemia. Some observations that are not necessarily in line with the model proposed should also be discussed.

The authors show that Dapa preserves serum ApoM and cardiac function in LPS-treated obese mice. However, the evidence they provide to suggest that ApoM may be implicated in the protective effect of Dapa on cardiac function is indirect. Direct evidence could be sought by addressing the effect of Dapa on cardiac function in LPS treated ApoM deficient and littermate control mice (with DIO if necessary).

The authors also suggest that higher ApoM levels in mice treated with Dapa and LPS reflect increased megalin-mediated ApoM reabsorption and that this preserves S1PR signaling. This could be addressed more directly by assessing the clearance of labelled ApoM, by addressing the impact of megalin inhibition or deficiency on ApoM clearance in this context, and by measuring S1P as well as ApoM in serum samples.

Methods: More details should be provided in the manuscript for how ApoM deficient and transgenic mice were generated, on sex and strain background, and on whether or not littermate controls were used. For intravital microscopy, more precision is needed on how vessel borders were outland and if this was done with or without regard for FITC-dextran. Please also specify the type of vessel chosen and considerations made with regard to blood flow and patency of the vessels analyzed. For statistical analyses, data from each mouse should be pooled before performing statistical comparisons. The criteria used for choice of test should be outlined as different statistical tests are used for similar datasets. For all data, please be consistent in the use of post-tests and in the presentation of comparisons. In other words, if the authors choose to only display test results for groups that are significantly different, this should be done in all cases. And if comparisons are made between all groups, this should be done in all cases for similar sets of data.

Thank you for these comments. We have now tested the direct role of Lrp2 with respect to SGLT2i in vivo and in vitro, and our study now shows that Lrp2 is required for the effect of dapagliflozin on ApoM. ApoM deficient and transgenic mice were previously described and published by our group (PMID: 37034289) and others (PMID: 24318881), and littermate controls were used throughout our manuscript. We agree that the effect on cardiac function is likely indirect in these models, and as yet we do not have the tools in the LPS model to separate potential endothelial protective vs cardiac effects. In addition, since the ApoM knockout has multiple abnormalities that include hypertension, secondary cardiac hypertrophy, and an adipose/browning phenotype, all of which may influence its response to Dapa in terms of cardiac function, these studies will be challenging to perform and will require additional models that are beyond the scope of this manuscript.

For intravital microscopy, vessel borders were outlined blindly without regard for FITC-dextran. We believe it is important to show multiple blood vessels per mouse since, as the reviewer points out, there is quite a bit of vessel heterogeneity. These tests were performed in the collaborator’s laboratory, and data analysis was blinded, and the collaborator was unaware of the study hypothesis at the time the measurements were performed and analyzed. They have previously reported this is a valid method to show cremaster vessel permeability (PMID: 26839042).

We have updated our methods section and updated the figure legends to clearly indicate the statistical analyses we used. For 2 group comparison we used student’s t-test, and for multiple groups one-way ANOVA with Sidak's correction for multiple comparisons was used throughout the paper when the data are normally distributed, and Kruskal-Wallis was used when the data are not normally distributed.

Reviewer #3 (Public Review):

The authors have performed well designed experiments that elucidate the protective role of Dapa in sepsis model of LPS. This model shows that Dapa works, in part, by increasing expression of the receptor LRP2 in the kidney, that maintains circulating ApoM levels. ApoM binds to S1P which then interacts with the S1P receptor stimulating cardiac function, epithelial and endothelial barrier function, thereby maintaining intravascular volume and cardiac output in the setting of severe inflammation. The authors used many experimental models, including transgenic mice, as well as several rigorous and reproducible techniques to measure the relevant parameters of cardiac, renal, vascular, and immune function. Furthermore, they employ a useful inhibitor of S1P function to show pharmacologically the essential role for this agonist in most but not all the benefits of Dapa. A strength of the paper is the identification of the pathway responsible for the cardioprotective effects of SGLT2is that may yield additional therapeutic targets. There are some weaknesses in the paper, such as, studying only male mice, as well as providing a power analysis to justify the number of animals used throughout their experimentation. Overall, the paper should have a significant impact on the scientific community because the SGLT2i drugs are likely to find many uses in inflammatory diseases and metabolic diseases. This paper provides support for an important mechanism by which they work in conditions of severe sepsis and hemodynamic compromise.

Thank you for these comments.

Author response:

Reviewer #1 (Public Review):

This paper proposes a novel framework for explaining patterns of generalization of force field learning to novel limb configurations. The paper considers three potential coordinate systems: cartesian, joint-based, and object-based. The authors propose a model in which the forces predicted under these different coordinate frames are combined according to the expected variability of produced forces. The authors show, across a range of changes in arm configurations, that the generalization of a specific force field is quite well accounted for by the model.

The paper is well-written and the experimental data are very clear. The patterns of generalization exhibited by participants - the key aspect of the behavior that the model seeks to explain - are clear and consistent across participants. The paper clearly illustrates the importance of considering multiple coordinate frames for generalization, building on previous work by Berniker and colleagues (JNeurophys, 2014). The specific model proposed in this paper is parsimonious, but there remain a number of questions about its conceptual premises and the extent to which its predictions improve upon alternative models.

A major concern is with the model's premise. It is loosely inspired by cue integration theory but is really proposed in a fairly ad hoc manner, and not really concretely founded on firm underlying principles. It's by no means clear that the logic from cue integration can be extrapolated to the case of combining different possible patterns of generalization. I think there may in fact be a fundamental problem in treating this control problem as a cue-integration problem. In classic cue integration theory, the various cues are assumed to be independent observations of a single underlying variable. In this generalization setting, however, the different generalization patterns are NOT independent; if one is true, then the others must inevitably not be. For this reason, I don't believe that the proposed model can really be thought of as a normative or rational model (hence why I describe it as 'ad hoc'). That's not to say it may not ultimately be correct, but I think the conceptual justification for the model needs to be laid out much more clearly, rather than simply by alluding to cue-integration theory and using terms like 'reliability' throughout.

We thank the reviewer for bringing up this point. We see and treat this problem of finding the combination weights not as a cue integration problem but as an inverse optimal control problem. In this case, there can be several solutions to the same problem, i.e., what forces are expected in untrained areas, which can co-exist and give the motor system the option to switch or combine them. This is similar to other inverse optimal control problems, e.g. combining feedforward optimal control models to explain simple reaching. However, compared to these problems, which fit the weights between different models, we proposed an explanation for the underlying principle that sets these weights for the dynamics representation problem. We found that basing the combination on each motor plan's reliability can best explain the results. In this case, we refer to ‘reliability’ as execution reliability and not sensory reliability, which is common in cue integration theory. We have added further details explaining this in the manuscript.

“We hypothesize that this inconsistency in results can be explained using a framework inspired by an inverse optimal control framework. In this framework the motor system can switch or combine between different solutions. That is, the motor system assigns different weights to each solution and calculates a weighted sum of these solutions. Usually, to support such a framework, previous studies found the weights by fitting the weighed sum solution to behavioral data (Berret, Chiovetto et al. 2011). While we treat the problem in the same manner, we propose the Reliable Dynamics Representation (Re-Dyn) mechanism that determines the weights instead of fitting them. According to our framework, the weights are calculated by considering the reliability of each representation during dynamic generalization. That is, the motor system prefers certain representations if the execution of forces based on this representation is more robust to distortion arising from neural noise. In this process, the motor system estimates the difference between the desired generalized forces and generated generalized forces while taking into consideration noise added to the state variables that equivalently define the forces.”

A more rational model might be based on Bayesian decision theory. Under such a model, the motor system would select motor commands that minimize some expected loss, averaging over the various possible underlying 'true' coordinate systems in which to generalize. It's not entirely clear without developing the theory a bit exactly how the proposed noise-based theory might deviate from such a Bayesian model. But the paper should more clearly explain the principles/assumptions of the proposed noise-based model and should emphasize how the model parallels (or deviates from) Bayesian-decision-theory-type models.

As we understand the reviewer's suggestion, the idea is to estimate the weight of each coordinate system based on minimizing a loss function that considers the cost of each weight multiplied by a posterior probability that represents the uncertainty in this weight value. While this is an interesting idea, we believe that in the current problem, there are no ‘true’ weight values. That is, the motor system can use any combination of weights which will be true due to the ambiguous nature of the environment. Since the force field was presented in one area of the entire workspace, there is no observation that will allow us to update prior beliefs regarding the force nature of the environment. In such a case, the prior beliefs might play a role in the loss function, but in our opinion, there is no clear rationale for choosing unequal priors except guessing or fitting prior probabilities, which will resemble any other previous models that used fitting rather than predictions.

Another significant weakness is that it's not clear how closely the weighting of the different coordinate frames needs to match the model predictions in order to recover the observed generalization patterns. Given that the weighting for a given movement direction is over- parametrized (i.e. there are 3 variable weights (allowing for decay) predicting a single observed force level, it seems that a broad range of models could generate a reasonable prediction. It would be helpful to compare the predictions using the weighting suggested by the model with the predictions using alternative weightings, e.g. a uniform weighting, or the weighting for a different posture. In fact, Fig. 7 shows that uniform weighting accounts for the data just as well as the noise-based model in which the weighting varies substantially across directions. A more comprehensive analysis comparing the proposed noise-based weightings to alternative weightings would be helpful to more convincingly argue for the specificity of the noise-based predictions being necessary. The analysis in the appendix was not that clearly described, but seemed to compare various potential fitted mixtures of coordinate frames, but did not compare these to the noise-based model predictions.

We agree with the reviewer that fitted global weights, that is, an optimal weighted average of the three coordinate systems should outperform most of the models that are based on prediction instead of fitting the data. As we showed in Figure 7 of the submitted version of the manuscript, we used the optimal fitted model to show that our noise-based model is indeed not optimal but can predict the behavioral results and not fall too short of a fitted model. When trying to fit a model across all the reported experiments, we indeed found a set of values that gives equal weights for the joints and object coordinate systems (0.27 for both), and a lower value for the Cartesian coordinate system (0.12). Considering these values, we indeed see how the reviewer can suggest a model that is based on equal weights across all coordinate systems. While this model will not perform as well as the fitted model, it can still generate satisfactory results.

To better understand if a model based on global weights can explain the combination between coordinate systems, we perform an additional experiment. In this experiment, a model that is based on global fitted weights can only predict one out of two possible generalization patterns while models that are based on individual direction-predicted weights can predict a variety of generalization patterns. We show that global weights, although fitted to the data, cannot explain participants' behavior. We report these new results in Appendix 2.

“To better understand if a model based on global weights can explain the combination between coordinate systems, we perform an additional experiment. We used the idea of experiment 3 in which participants generalize learned dynamics using a tool. That is, the arm posture does not change between the training and test areas. In such a case, the Cartesian and joint coordinate systems do not predict a shift in generalized force pattern while the object coordinate system predicts a shift that depends on the orientation of the tool. In this additional experiment, we set a test workspace in which the orientation of the tool is 90° (Appendix 2- figure 1A). In this case, for the test workspace, the force compensation pattern of the object based coordinate system is in anti-phase with the Cartesian/joint generalization pattern. Any globally fitted weights (including equal weights) can produce either a non-shifted or 90° shifted force compensation pattern (Appendix 2- figure 1B). Participants in this experiment (n=7) showed similar MPE reduction as in all previous experiments when adapting to the trigonometric scaled force field (Appendix 2- figure 1C). When examining the generalized force compensation patterns, we observed a shift of the pattern in the test workspace of 14.6° (Appendix 2- figure 1D). This cannot be explained by the individual coordinate system force compensation patterns or any combination of them (which will always predict either a 0° or 90° shift, Appendix 2- figure 1E). However, calculating the prediction of the Re-Dyn model we found a predicted force compensation pattern with a shift of 6.4° (Appendix 2- figure 1F). The intermediate shift in the force compensation pattern suggests that any global based weights cannot explain the results.”

With regard to the suggestion that weighting is changed according to arm posture, two of our results lower the possibility that posture governs the weights:

(1) In experiment 3, we tested generalization while keeping the same arm posture between the training and test workspaces, and we observed different force compensation profiles across the movement directions. If arm posture in the test workspaces affected the weights, we would expect identical weights for both test workspaces. However, any set of weights that can explain the results observed for workspace 1 will fail to explain the results observed in workspace 2. To better understand this point we calculated the global weights for each test workspace for this experiment and we observed an increase in the weight for the object coordinates system (0.41 vs. 0.5) and a reduction in the weights for the Cartesian and joint coordinates systems (0.29 vs. 0.24). This suggests that the arm posture cannot explain the generalization pattern in this case.

(2) In experiments 2 and 3, we used the same arm posture in the training workspace and either changed the arm posture (experiment 2) or did not change the arm posture (experiment 3) in the test workspaces. While the arm posture for the training workspace was the same, the force generalization patterns were different between the two experiments, suggesting that the arm posture during the training phase (adaptation) does not set the generalization weights.

Overall, this shows that it is not specifically the arm posture in either the test or the training workspaces that set the weights. Of course, all coordinate models, including our noise model, will consider posture in the determination of the weights.

Reviewer #2 (Public Review):

Leib & Franklin assessed how the adaptation of intersegmental dynamics of the arm generalizes to changes in different factors: areas of extrinsic space, limb configurations, and 'object-based' coordinates. Participants reached in many different directions around 360{degree sign}, adapting to velocity-dependent curl fields that varied depending on the reach angle. This learning was measured via the pattern of forces expressed in upon the channel wall of "error clamps" that were randomly sampled from each of these different directions. The authors employed a clever method to predict how this pattern of forces should change if the set of targets was moved around the workspace. Some sets of locations resulted in a large change in joint angles or object-based coordinates, but Cartesian coordinates were always the same. Across three separate experiments, the observed shifts in the generalized force pattern never corresponded to a change that was made relative to any one reference frame. Instead, the authors found that the observed pattern of forces could be explained by a weighted combination of the change in Cartesian, joint, and object-based coordinates across test and training contexts.

In general, I believe the authors make a good argument for this specific mixed weighting of different contexts. I have a few questions that I hope are easily addressed.

Movements show different biases relative to the reach direction. Although very similar across people, this function of biases shifts when the arm is moved around the workspace (Ghilardi, Gordon, and Ghez, 1995). The origin of these biases is thought to arise from several factors that would change across the different test and training workspaces employed here (Vindras & Viviani, 2005). My concern is that the baseline biases in these different contexts are different and that rather the observed change in the force pattern across contexts isn't a function of generalization, but a change in underlying biases. Baseline force channel measurements were taken in the different workspace locations and conditions, so these could be used to show whether such biases are meaningfully affecting the results.

We agree with the reviewer and we followed their suggested analysis. In the following figure (Author response image 1) we plotted the baseline force compensation profiles in each workspace for each of the four experiments. As can be seen in this figure, the baseline force compensation is very close to zero and differs significantly from the force compensation profiles after adaptation to the scaled force field.

Author response image 1.

Baseline force compensation levels for experiments 1-4. For each experiment, we plotted the force compensation for the training, test 1, and test 2 workspaces.

Experiment 3, Test 1 has data that seems the worst fit with the overall story. I thought this might be an issue, but this is also the test set for a potentially awkwardly long arm. My understanding of the object-based coordinate system is that it's primarily a function of the wrist angle, or perceived angle, so I am a little confused why the length of this stick is also different across the conditions instead of just a different angle. Could the length be why this data looks a little odd?

Usually, force generalization is tested by physically moving the hand in unexplored areas. In experiment 3 we tested generalization using a tool which, as far as we know, was not tested in the past in a similar way to the present experiment. Indeed, the results look odd compared to the results of the other experiments, which were based on the ‘classic’ generalization idea. While we have some ideas regarding possible reasons for the observed behavior, it is out of the scope of the current work and still needs further examination.

Based on the reviewer’s comment, we improved the explanation in the introduction regarding the idea behind the object based coordinate system

“we could represent the forces as belonging to the hand or a hand-held object using the orientation vector connecting the shoulder and the object or hand in space (Berniker, Franklin et al. 2014).” The reviewer is right in their observation that the predictions of the object-based reference frame will look the same if we change the length of the tool. The object-based generalized forces, specifically the shift in the force pattern, depend only on the object's orientation but not its length (equation 4).

The manuscript is written and organized in a way that focuses heavily on the noise element of the model. Other than it being reasonable to add noise to a model, it's not clear to me that the noise is adding anything specific. It seems like the model makes predictions based on how many specific components have been rotated in the different test conditions. I fear I'm just being dense, but it would be helpful to clarify whether the noise itself (and inverse variance estimation) are critical to why the model weights each reference frame how it does or whether this is just a method for scaling the weight by how much the joints or whatever have changed. It seems clear that this noise model is better than weighting by energy and smoothness.

We have now included further details of the noise model and added to Figure 1 to highlight how noise can affect the predicted weights. In short, we agree with the reviewer there are multiple ways to add noise to the generalized force patterns. We choose a simple option in which we simulate possible distortions to the state variables that set the direction of movement. Once we calculated the variance of the force profile due to this distortion, one possible way is to combine them using an inverse variance estimator. Note that it has been shown that an inverse variance estimator is an ideal way to combine signals (e.g., Shahar, D.J. (2017) https://doi.org/10.4236/ojs.2017.72017). However, as we suggest, we do not claim or try to provide evidence for this specific way of calculating the weights. Instead, we suggest that giving greater weight to the less variable force representation can predict both the current experimental results as well as past results.

Are there any force profiles for individual directions that are predicted to change shape substantially across some of these assorted changes in training and test locations (rather than merely being scaled)? If so, this might provide another test of the hypotheses.

In experiments 1-3, in which there is a large shift of the force compensation curve, we found directions in which the generalized force was flipped in direction. That is, clockwise force profiles in the training workspace could change into counter-clockwise profiles in the test workspace. For example, in experiment 2, for movement at 157.5° we can see that the force profile was clockwise for the training workspace (with a force compensation value of 0.43) and movement at the same direction was counterclockwise for test workspace 1 (force compensation equal to -0.48). Importantly, we found that the noise based model could predict this change.

Author response image 2.

Results of experiment 2. Force compensation profiles for the training workspace (grey solid line) and test workspace 1 (dark blue solid line). Examining the force nature for the 157.5° direction, we found a change in the applied force by the participants (change from clockwise to counterclockwise forces). This was supported by a change in force compensation value (0.43 vs. -0.48). The noise based model can predict this change as shown by the predicted force compensation profile (green dashed line).

I don't believe the decay factor that was used to scale the test functions was specified in the text, although I may have just missed this. It would be a good idea to state what this factor is where relevant in the text.

We added an equation describing the decay factor (new equation 7 in the Methods section) according to this suggestion and Reviewer 1 comment on the same issue.

Reviewer #3 (Public Review):

The author proposed the minimum variance principle in the memory representation in addition to two alternative theories of the minimum energy and the maximum smoothness. The strength of this paper is the matching between the prediction data computed from the explicit equation and the behavioral data taken in different conditions. The idea of the weighting of multiple coordinate systems is novel and is also able to reconcile a debate in previous literature.

The weakness is that although each model is based on an optimization principle, but the derivation process is not written in the method section. The authors did not write about how they can derive these weighting factors from these computational principles. Thus, it is not clear whether these weighting factors are relevant to these theories or just hacking methods. Suppose the author argues that this is the result of the minimum variance principle. In that case, the authors should show a process of how to derive these weighting factors as a result of the optimization process to minimize these cost functions.

The reviewer brings up a very important point regarding the model. As shown below, it is not trivial to derive these weights using an analytical optimization process. We demonstrate one issue with this optimization process.

The force representation can be written as (similar to equation 6):

We formulated the problem as minimizing the variance of the force according to the weights w:

In this case, the variance of the force is the variance-covariance matrix which can be minimized by minimizing the matrix trace:

We will start by calculating the variance of the force representation in joints coordinate system:

Here, the force variance is a result of a complex function which include the joints angle as a random variable. Expending the last expression, although very complex, is still possible. In the resulted expression, some of the resulted terms include calculating the variance of nested trigonometric functions of the random joint angle variance, for example:

In the vast majority of these cases, analytical solutions do not exist. Similar issues can also raise for calculating the variance of complex multiplication of trigonometric functions such as in the case of multiplication of Jacobians (and inverse Jacobians)

To overcome this problem, we turned to numerical solutions which simulate the variance due to the different state variables.

In addition, I am concerned that the proposed model can cancel the property of the coordinate system by the predicted variance, and it can work for any coordinate system, even one that is not used in the human brain. When the applied force is given in Cartesian coordinates, the directionality in the generalization ability of the memory of the force field is characterized by the kinematic relationship (Jacobian) between the Cartesian coordinate and the coordinate of interest (Cartesian, joint, and object) as shown in Equation 3. At the same time, when a displacement (epsilon) is considered in a space and a corresponding displacement is linked with kinematic equations (e.g., joint displacement and hand displacement in 2 joint arms in this paper), the generated variances in different coordinate systems are linked with the kinematic equation each other (Jacobian). Thus, how a small noise in a certain coordinate system generates the hand force noise (sigma_x, sigma_j, sigma_o) is also characterized by the kinematics (Jacobian). Thus, when the predicted forcefield (F_c, F_j, F_o) was divided by the variance (F_c/sigma_c^2, F_j/sigma_j^2, F_o/sigma_o^2, ), the directionality of the generalization force which is characterized by the Jacobian is canceled by the directionality of the sigmas which is characterized by the Jacobian. Thus, as it has been read out from Fig*D and E top, the weight in E-top of each coordinate system is always the inverse of the shift of force from the test force by which the directionality of the generalization is always canceled.

Once this directionality is canceled, no matter how to compute the weighted sum, it can replicate the memorized force. Thus, this model always works to replicate the test force no matter which coordinate system is assumed. Thus, I am suspicious of the falsifiability of this computational model. This model is always true no matter which coordinate system is assumed. Even though they use, for instance, the robot coordinate system, which is directly linked to the participant's hand with the kinematic equation (Jacobian), they can replicate this result. But in this case, the model would be nonsense. The falsifiability of this model was not explicitly written.

As explained above, calculating the variability of the generalized forces given the random nature of the state variable is a complex function that is not summarized using a Jacobian. Importantly the model is unable to reproduce or replicate the test force arbitrarily. In fact, we have already shown this (see Appendix 1- figure 1), where when we only attempt to explain the data with either a single coordinate system (or a combination of two coordinate systems) we are completely unable to replicate the test data despite using this model. For example, in experiment 4, when we don’t use the joint based coordinate system, the model predicts zero shift of the force compensation pattern while the behavioral data show a shift due to the contribution of the joint coordinate system. Any arbitrary model (similar to the random model we tested, please see the response to Reviewer 1) would be completely unable to recreate the test data. Our model instead makes very specific predictions about the weighting between the three coordinate systems and therefore completely specified force predictions for every possible test posture. We added this point to the Discussion

“The results we present here support the idea that the motor system can use multiple representations during adaptation to novel dynamics. Specifically, we suggested that we combine three types of coordinate systems, where each is independent of the other (see Appendix 1- figure 1 for comparison with other combinations). Other combinations that include a single or two coordinate system can explain some of the results but not all of them, suggesting that force representation relies on all three with specific weights that change between generalization scenarios.”

Deze criteria helpen neuropsychologen om op een betrouwbare manier te beoordelen of verschillende gegevens samenhangend zijn, een proces dat ook wel een coherentieanalyse wordt genoemd. De "zeven C's" helpen de onderzoeker om een goed beeld te krijgen van de klinische situatie van de patiënt.

Continuïteit: Dit betekent dat de symptomen van de patiënt zich ontwikkelen op een manier die je zou verwachten op basis van wat wetenschappelijk bekend is over de aandoening. Bijvoorbeeld, als een aandoening volgens de wetenschap verergert na verloop van tijd, dan moet dit ook bij de patiënt zichtbaar zijn.

Consistentie van presentatie over tijd: Dit verwijst naar hoe stabiel of consistent de symptomen van de patiënt zijn. De manier waarop de patiënt zijn of haar klachten presenteert, zou in de loop van de tijd niet drastisch moeten veranderen zonder duidelijke reden.

Congruentie: Dit betekent dat de verschillende aspecten van de klachten en het klinische beeld van de patiënt met elkaar moeten kloppen. Verschillende symptomen of gedragingen moeten logisch in elkaar passen en elkaar niet tegenspreken.

Compliance (medewerking aan behandeling): Dit kijkt naar hoe goed de patiënt de behandeling opvolgt. Als een patiënt zich niet aan het voorgeschreven behandelplan houdt, kan dit invloed hebben op de klinische uitkomst en moet dit worden meegewogen in de beoordeling.

Causaliteit: Hier wordt gekeken of de aandoening daadwerkelijk de oorzaak is van de klachten van de patiënt, of dat er een andere of aanvullende oorzaak kan zijn die de symptomen beter verklaart.

Comorbiditeiten: Dit verwijst naar het controleren of er andere aandoeningen of stoornissen (comorbiditeiten) aanwezig zijn die de klachten van de patiënt kunnen verklaren. Deze kunnen soms de hoofdklacht verergeren of de symptomen verwarrend maken.

Culturele factoren: Culturele achtergronden kunnen invloed hebben op hoe een patiënt zijn of haar klachten presenteert. Het is belangrijk om te overwegen of de cultuur van de patiënt een rol speelt in hoe de symptomen worden ervaren en gecommuniceerd.

Niet-zichtbare inspanning komt niet vaak voor, maar als het gebeurt, gebeurt het redelijk consistent. Dit betekent dat het soms moeilijk is om te zien of iemand wel echt zijn best doet bij een taak of test. Vooral bij kinderen met aanhoudende klachten na een lichte hoofdwond komt dit vaker voor. Meer dan 60% van de kinderen die psychologische tests doen om te kijken of ze ergens voor in aanmerking komen, vertoont tekenen van simulatie (doen alsof ze zieker zijn dan ze werkelijk zijn). In veel gevallen komt dit door de ouders, die hun kinderen aanmoedigen om zieker te lijken (malingering by proxy). Er is echter niet veel onderzoek naar dit onderwerp gedaan, dus we weten er nog niet alles over.

Bij kinderen is het niet goed meedoen aan tests vaak te wijten aan familieomstandigheden, vooral bij kinderen met een lichte hoofdwond. Dit maakt het anders dan bij volwassenen, waar financiële redenen (zoals geld krijgen van verzekeringen) vaak de reden zijn waarom ze niet hun best doen tijdens tests.

Repressietheoretici denken dat onderdrukte herinneringen aan trauma niet onschuldig zijn, maar ongemerkt het emotionele leven van mensen beïnvloeden. We testten of mensen met onderdrukte herinneringen trager trauma-gerelateerde woorden konden benoemen in de emotionele Stroop-test. Mensen met PTSS hebben vaak moeite met het snel benoemen van bedreigende woorden, omdat deze hun aandacht trekken. Daarnaast onderzochten we of mensen moeite hadden met het herinneren van specifieke gebeurtenissen. Moeite met het ophalen van specifieke herinneringen kan wijzen op problemen bij het verwerken van emotioneel moeilijke ervaringen, wat kan bijdragen aan depressie. Mensen met onderdrukte herinneringen hebben vaak ook deze moeite, en dit bleek inderdaad zo te zijn.

In gevallen van psychogene amnesie zijn de oorzaken van het geheugenverlies vaak niet gerelateerd aan een trauma. In plaats daarvan kunnen ze te maken hebben met psychologische factoren die geen direct verband houden met een specifieke traumatische gebeurtenis. Daarentegen wordt traumatische dissociatieve amnesie juist veroorzaakt door een traumatische ervaring, zoals misbruik of een ongeluk.

1. Onopgemerkte lichamelijke pathologie: Lichamelijke aandoeningen die niet zijn gedetecteerd of correct zijn gediagnosticeerd.

2. Somatisatiestoornis: Een patroon van terugkerende, meerdere lichamelijke symptomen die leiden tot medische behandeling of beperkingen in het dagelijks functioneren. Dit begint meestal voor het 30e levensjaar en kan langdurig aanwezig zijn. Het is een van de vijf stoornissen die onderscheiden moeten worden van malingering (simulatie).

3. Hypochondrie: Gericht op de angst om een ernstige ziekte te hebben, gebaseerd op de verkeerde interpretatie van lichamelijke symptomen. Deze mensen willen vaak meerdere testen ondergaan en stemmen gemakkelijk in met allerlei onderzoeken. Simulanten (malingerers) werken meestal niet mee, omdat ze weten dat negatieve testresultaten bij hen hoogst onwaarschijnlijk zijn. Hypochondrische patiënten veroorzaken soms ook zelf ziektesymptomen.

4. Pijnstoornis: Er zijn twee categorieën binnen deze stoornis:

5. Pijnstoornis met gerelateerde psychologische factoren.

6. Pijnstoornis die alleen verband houdt met psychologische factoren.

7. Factitious disorder (FD) met voornamelijk fysieke tekenen en symptomen: Fysieke of psychologische symptomen worden opzettelijk veroorzaakt om de rol van de zieke aan te nemen. Er is geen externe prikkel (zoals financieel gewin). Deze individuen vertonen geloofwaardige lichamelijke symptomen.

Rand en Feldman benadrukken dat snelle en grondige onderzoeken essentieel zijn wanneer er een vermoeden is van Munchausen by Proxy-syndroom (MBP). Volgens hen zou MBP alleen moeten worden gebruikt om een specifieke vorm van kindermishandeling te beschrijven, waarbij de ouder of verzorger bewust misleidt met het doel om emotionele bevrediging te krijgen. Dit betekent dat MBP niet gezien moet worden als een vast kenmerk van de ouder of het kind, maar als een bewuste handeling van misbruik. Ze waarschuwen ook voor het risico van verkeerde diagnoses wanneer artsen alleen vertrouwen op psychologische profielen. Dit kan ertoe leiden dat een ouder onterecht wordt beschuldigd van MBP, simpelweg omdat hij of zij voldoet aan bepaalde kenmerken die ook in andere situaties voorkomen, zonder dat er daadwerkelijk sprake is van MBP.

In de literatuur wordt tegenwoordig voorgesteld om het gedrag in kwestie te zien als een spectrum van gedragingen. Dit betekent dat er veel verschillende factoren zijn die een rol kunnen spelen bij de motivatie van iemand om dit gedrag te vertonen, en dat deze factoren zich over een breed scala kunnen bevinden. In plaats van één enkel motief of vaste oorzaak, kunnen er meerdere, complexe redenen zijn die samen bijdragen aan het misleidende of schadelijke gedrag. Hierdoor kunnen de beweegredenen per individu sterk variëren, en kan het gedrag uiteenlopende vormen aannemen, afhankelijk van de specifieke combinatie van factoren die bij die persoon een rol spelen.

Voordelen: helpt bij vroegtijdige identificatie, zorgt voor betere bescherming van het kind, bevordert samenwerking tussen professionals en biedt een duidelijke scheiding tussen medische en psychiatrische aspecten.

Factitious Disorder by Proxy (FDP): De specifieke psychiatrische diagnose van de dader en de motivatie achter het misbruik.

Pediatric Condition Falsification (PCF): Het erkennen dat de medische presentatie van het kind is vervalst.

Het pathogene model gaat ervan uit dat misleiding voortkomt uit een onderliggende psychische stoornis.

After WW2, communism is seen as the biggest threat to Western Society.

Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Genomics.<br /> See the peer reviews and the recommendation.

eLife Assessment

The specific questions taken up for study by the authors-in mice of HDAC and Polycomb function in the context of vascular endothelial cell (EC) gene expression relevant to the blood-brain barrier, (BBB)-are potentially useful in the context of vascular diversification in understanding and remedying situations where BBB function is compromised. The strength of the evidence presented is incomplete, and to elaborate, it is known that the culturing of endothelial cells can have a strong effect on gene expression.

Reviewer #1 (Public review):

The blood-brain barrier separates neural tissue from blood-borne factors and is important for maintaining central nervous system health and function. Endothelial cells are the site of the barrier. These cells exhibit unique features relative to peripheral endothelium and a unique pattern of gene expression. There remains much to be learned about how the transcriptome of brain endothelial cells is established in development and maintained throughout life.

The manuscript by Sadanandan, Thomas et al. investigates this question by examining transcriptional and epigenetic changes in brain endothelial cells in embryonic and adult mice. Changes in transcript levels and histone marks for various BBB-relevant transcripts, including Cldn5, Mfsd2a and Zic3 were observed between E13.5 and adult mice. To perform these experiments, endothelial cells were isolated from E13.5 and adult mice, then cultured in vitro, then sequenced. This approach is problematic. It is well-established that brain endothelial cells rapidly lose their organotypic features in culture (https://elifesciences.org/articles/51276). Indeed, one of the primary genes investigated in this study, Cldn1, exhibits very low expression at the transcript level in vivo, but is strongly upregulated in cultured ECs.

(https://elifesciences.org/articles/36187 ; https://markfsabbagh.shinyapps.io/vectrdb/)

This undermines the conclusions of the study. While this manuscript is framed as investigating how epigenetic processes shape BBB formation and maintenance, they may be looking at how brain endothelial cells lose their identity in culture.

An additional concern is that for many experiments, siRNA knockdowns are performed without validation of the efficacy of knockdown.

Some experiments in the paper are promising, however. For example, the knockout of HDAC2 in endothelial cells resulting in BBB leakage was striking. Investigating the mechanisms underlying this phenotype in vivo could yield important insights.

Reviewer #2 (Public review):

Sadanandan et al describe their studies in mice of HDAC and Polycomb function in the context of vascular endothelial cell (EC) gene expression relevant to the blood-brain barrier, (BBB). This topic is of interest because the BBB gene expression program represents an interesting and important vascular diversification mechanism. From an applied point of view, modifying this program could have therapeutic benefits in situations where BBB function is compromised.

The study involves comparing the transcriptomes of cultured CNS ECs at E13 and adult stages and then perturbing EC gene expression pharmacologically in cell culture (with HDAC and Polycomb inhibitors) and genetically in vivo by EC-specific conditional KO of HDAC2 and Polycomb component EZH2.

This reviewer has several critiques of the study.

First, based on published data, the effect of culturing CNS ECs is likely to have profound effects on their differentiation, especially as related to their CNS-specific phenotypes. Related to this, the authors do not state how long the cells were cultured.

Second, the use of qPCR assays for quantifying ChIP and transcript levels is inferior to ChIPseq and RNAseq. Whole genome methods, such as ChIPseq, permit a level of quality assessment that is not possible with qPCR methods. The authors should use whole genome NextGen sequencing approaches, show the alignment of reads to the genome from replicate experiments, and quantitatively analyze the technical quality of the data.

Third, the observation that pharmacologic inhibitor experiments and conditional KO experiments targeting HDAC2 and the Polycomb complex perturb EC gene expression or BBB integrity, respectively, is not particularly surprising as these proteins have broad roles in epigenetic regulation is a wide variety of cell types.

Author response:

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

Reviewers' 1 and 2 concern on endothelial cells (ECs) transcription changes on culture.

We have now addressed this concern by FACS-sorting ECs (Fig. 7A revised) and comparing our data with previous studies (S. Fig. 1C). Our major claim was the epigenetic repression of EC genes, including those involved in BBB formation and angiogenesis, during later development. To further strengthen our claim, we knocked out HDAC2 during the later stages of development to prevent this epigenetic repression. As shown in the first version of the manuscript, this knockout results in enhanced angiogenesis and a leaky BBB.

In the revised version, we have FACS-sorted CD31+ ECs from E-17.5 WT and HDAC2 ECKO mice, followed by ultra-low mRNA sequencing. Confirming the epigenetic repression via HDAC2, the HDAC2-deleted ECs showed high expression of BBB genes such as ZO-1, OCLN, MFSD2A, and GLUT1, and activation of the Wnt signaling pathway as indicated by the upregulation of Wnt target genes such as Axin2 and APCDD1. Additionally, to validate the increased angiogenesis phenotype observed, angiogenesis-related genes such as VEGFA, FLT1, and ENG were upregulated.

Since the transcriptomics of brain ECs during developmental stages has already been published in Hupe et al., 2017, we did not attempt to replicate this. However, we compared our differentially regulated genes from E-13.5 versus adult stages with the transcriptome changes during development reported by Hupe et al., 2017. We found a significant overlap in important genes such as CLDN5, LEF1, ZIC3, and MFSD2A (S. Fig. 1C).

As pointed out by the reviewer, culture-induced changes cannot be ruled out from our data. We have included a statement in the manuscript: "Even though we used similar culture conditions for both embryonic and adult cortical ECs, culture-induced changes have been reported previously and should be considered as a varying factor when interpreting our results."

Reviewer-1 Comment 2- An additional concern is that for many experiments, siRNA knockdowns are performed without validation of the efficacy of the knockdown.

We have now provided the protein expression data for HDAC2 and EZH2 in the revised manuscript Supplementary Figure- 2A.

Reviewer-1 Comment 3- Some experiments in the paper are promising, however. For example, the knockout of HDAC2 in endothelial cells resulting in BBB leakage was striking. Investigating the mechanisms underlying this phenotype in vivo could yield important insights.

We appreciate your positive comment. The in vivo HDAC2 knockout experiment serves as a validation of our in vitro findings, demonstrating that the epigenetic regulator HDAC2 can control the expression of endothelial cell (EC) genes involved in angiogenesis, blood-brain barrier (BBB) formation, and maturation. To investigate the mechanism behind the underlying phenotype of HDAC2 ECKO, we performed mRNA sequencing on HDAC2 ECKO E-17.5 ECs and discovered that vascular and BBB maturation is hindered by preventing the epigenetic repression of BBB, angiogenesis, and Wnt target genes (Fig. 7A). As a result, the HDAC2 ECKO phenotype showed increased angiogenesis and BBB leakage. This strengthens our hypothesis that HDAC2-mediated epigenetic repression is critical for BBB and vascular maturation.

Reviewer 2 Comment-2 The use of qPCR assays for quantifying ChIP and transcript levels is inferior to ChIPseq and RNAseq. Whole genome methods, such as ChIPseq, permit a level of quality assessment that is not possible with qPCR methods. The authors should use whole genome NextGen sequencing approaches, show the alignment of reads to the genome from replicate experiments, and quantitatively analyze the technical quality of the data.

We appreciate the reviewer's comment. While whole-genome methods like ChIP-seq offer comprehensive and high-throughput data, ChIP-qPCR assays remain valuable tools due to their sensitivity, specificity, and suitability for validation and targeted analysis. Our ChIP analysis identify the crucial roles of HDAC2 and PRC2, two epigenetic enzymes, in CNS endothelial cells (ECs). In vivo data presented in Figure 4 further support this finding through observed phenotypic differences. We concur that a comprehensive analysis of HDAC2 and PRC2 target genes in ECs is essential. A comprehensive analysis of HDAC2 and PRC2 target genes in ECs is currently underway and will be the subject of a separate publication due to the extensive nature of the data.

Reviewer 2 Comment-3 Third, the observation that pharmacologic inhibitor experiments and conditional KO experiments targeting HDAC2 and the Polycomb complex perturb EC gene expression or BBB integrity, respectively, is not particularly surprising as these proteins have broad roles in epigenetic regulation in a wide variety of cell types.

We appreciate the comments from the reviewers. Our results provide valuable insights into the specific epigenetic mechanisms that regulate BBB genes It is important to recognize that different cell types possess stage-specific distinct epigenetic landscapes and regulatory mechanisms. Rather than having broad roles across diverse cell types, it is more likely that HDAC2 (eventhough there are several other class and subtypes of HDACs) and the Polycomb complex exhibit specific functions within the context of EC gene expression or BBB integrity.

Moreover, the significance of our findings is enhanced by the fact that epigenetic modifications are often reversible with the assistance of epigenetic regulators. This makes them promising targets for BBB modulation. Targeting epigenetic regulators can have a widespread impact, as these mechanisms regulate numerous genes that collectively have the potential to promote the vascular repair.

A practical advantage is that FDA-approved HDAC2 inhibitors, as well as PRC2 inhibitors (such as those mentioned in clinical trials NCT03211988 and NCT02601950, are already available. This facilitates the repurposing of drugs and expedites their potential for clinical translation.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors address whether the dorsal nucleus of the inferior colliculus (DCIC) in mice encodes sound source location within the front horizontal plane (i.e., azimuth). They do this using volumetric two-photon Ca2+ imaging and high-density silicon probes (Neuropixels) to collect single-unit data. Such recordings are beneficial because they allow large populations of simultaneous neural data to be collected. Their main results and the claims about those results are the following:

(1) DCIC single-unit responses have high trial-to-trial variability (i.e., neural noise);

(2) approximately 32% to 40% of DCIC single units have responses that are sensitive tosound source azimuth;

(3) single-trial population responses (i.e., the joint response across all sampled single unitsin an animal) encode sound source azimuth "effectively" (as stated in title) in that localization decoding error matches average mouse discrimination thresholds;

(4) DCIC can encode sound source azimuth in a similar format to that in the central nucleusof the inferior colliculus (as stated in Abstract);

(5) evidence of noise correlation between pairs of neurons exists;

and 6) noise correlations between responses of neurons help reduce population decoding error.

While simultaneous recordings are not necessary to demonstrate results #1, #2, and #4, they are necessary to demonstrate results #3, #5, and #6.

Strengths:

- Important research question to all researchers interested in sensory coding in the nervous system.

- State-of-the-art data collection: volumetric two-photon Ca2+ imaging and extracellularrecording using high-density probes. Large neuronal data sets.

- Confirmation of imaging results (lower temporal resolution) with more traditionalmicroelectrode results (higher temporal resolution).

- Clear and appropriate explanation of surgical and electrophysiological methods. I cannot comment on the appropriateness of the imaging methods.

Strength of evidence for claims of the study:

(1) DCIC single-unit responses have high trial-to-trial variability - The authors' data clearlyshows this.

(2) Approximately 32% to 40% of DCIC single units have responses that are sensitive tosound source azimuth - The sensitivity of each neuron's response to sound source azimuth was tested with a Kruskal-Wallis test, which is appropriate since response distributions were not normal. Using this statistical test, only 8% of neurons (median for imaging data) were found to be sensitive to azimuth, and the authors noted this was not significantly different than the false positive rate. The Kruskal-Wallis test was not performed on electrophysiological data. The authors suggested that low numbers of azimuth-sensitive units resulting from the statistical analysis may be due to the combination of high neural noise and relatively low number of trials, which would reduce statistical power of the test. This may be true, but if single-unit responses were moderately or strongly sensitive to azimuth, one would expect them to pass the test even with relatively low statistical power. At best, if their statistical test missed some azimuthsensitive units, they were likely only weakly sensitive to azimuth. The authors went on to perform a second test of azimuth sensitivity-a chi-squared test-and found 32% (imaging) and 40% (e-phys) of single units to have statistically significant sensitivity. This feels a bit like fishing for a lower p-value. The Kruskal-Wallis test should have been left as the only analysis. Moreover, the use of a chi-squared test is questionable because it is meant to be used between two categorical variables, and neural response had to be binned before applying the test.

The determination of what is a physiologically relevant “moderate or strong azimuth sensitivity” is not trivial, particularly when comparing tuning across different relays of the auditory pathway like the CNIC, auditory cortex, or in our case DCIC, where physiologically relevant azimuth sensitivities might be different. This is likely the reason why azimuth sensitivity has been defined in diverse ways across the bibliography (see Groh, Kelly & Underhill, 2003 for an early discussion of this issue). These diverse approaches include reaching a certain percentage of maximal response modulation, like used by Day et al. (2012, 2015, 2016) in CNIC, and ANOVA tests, like used by Panniello et al. (2018) and Groh, Kelly & Underhill (2003) in auditory cortex and IC respectively. Moreover, the influence of response variability and biases in response distribution estimation due to limited sampling has not been usually accounted for in the determination of azimuth sensitivity.

As Reviewer #1 points out, in our study we used an appropriate ANOVA test (KruskalWallis) as a starting point to study response sensitivity to stimulus azimuth at DCIC. Please note that the alpha = 0.05 used for this test is not based on experimental evidence about physiologically relevant azimuth sensitivity but instead is an arbitrary p-value threshold. Using this test on the electrophysiological data, we found that ~ 21% of the simultaneously recorded single units reached significance (n = 4 mice). Nevertheless these percentages, in our small sample size (n = 4) were not significantly different from our false positive detection rate (p = 0.0625, Mann-Whitney, See Author response image 1 below).  In consequence, for both our imaging (Fig. 3C) and electrophysiological data, we could not ascertain if the percentage of neurons reaching significance in these ANOVA tests were indeed meaningfully sensitive to azimuth or this was due to chance. 

Author response image 1.

Percentage of the neuropixels recorded DCIC single units across mice that showed significant median response tuning, compared to false positive detection rate (α = 0.05, chance level).

We reasoned that the observed markedly variable responses from DCIC units, which frequently failed to respond in many trials (Fig. 3D, 4A), in combination with the limited number of trial repetitions we could collect, results in under-sampled response distribution estimations. This under-sampling can bias the determination of stochastic dominance across azimuth response samples in Kruskal-Wallis tests. We would like to highlight that we decided not to implement resampling strategies to artificially increase the azimuth response sample sizes with “virtual trials”, in order to avoid “fishing for a smaller p-value”, when our collected samples might not accurately reflect the actual response population variability.

As an alternative to hypothesis testing based on ranking and determining stochastic dominance of one or more azimuth response samples (Kruskal-Wallis test), we evaluated the overall statistical dependency to stimulus azimuth of the collected responses.  To do this we implement the Chi-square test by binning neuronal responses into categories. Binning responses into categories can reduce the influence of response variability to some extent, which constitutes an advantage of the Chi-square approach, but we note the important consideration that these response categories are arbitrary.

Altogether, we acknowledge that our Chi-square approach to define azimuth sensitivity is not free of limitations and despite enabling the interrogation of azimuth sensitivity at DCIC, its interpretability might not extend to other brain regions like CNIC or auditory cortex. Nevertheless we hope the aforementioned arguments justify why the Kruskal-Wallis test simply could not “have been left as the only analysis”.

(3) Single-trial population responses encode sound source azimuth "effectively" in that localization decoding error matches average mouse discrimination thresholds - If only one neuron in a population had responses that were sensitive to azimuth, we would expect that decoding azimuth from observation of that one neuron's response would perform better than chance. By observing the responses of more than one neuron (if more than one were sensitive to azimuth), we would expect performance to increase. The authors found that decoding from the whole population response was no better than chance. They argue (reasonably) that this is because of overfitting of the decoder modeltoo few trials used to fit too many parameters-and provide evidence from decoding combined with principal components analysis which suggests that overfitting is occurring. What is troubling is the performance of the decoder when using only a handful of "topranked" neurons (in terms of azimuth sensitivity) (Fig. 4F and G). Decoder performance seems to increase when going from one to two neurons, then decreases when going from two to three neurons, and doesn't get much better for more neurons than for one neuron alone. It seems likely there is more information about azimuth in the population response, but decoder performance is not able to capture it because spike count distributions in the decoder model are not being accurately estimated due to too few stimulus trials (14, on average). In other words, it seems likely that decoder performance is underestimating the ability of the DCIC population to encode sound source azimuth.

To get a sense of how effective a neural population is at coding a particular stimulus parameter, it is useful to compare population decoder performance to psychophysical performance. Unfortunately, mouse behavioral localization data do not exist. Therefore, the authors compare decoder error to mouse left-right discrimination thresholds published previously by a different lab. However, this comparison is inappropriate because the decoder and the mice were performing different perceptual tasks. The decoder is classifying sound sources to 1 of 13 locations from left to right, whereas the mice were discriminating between left or right sources centered around zero degrees. The errors in these two tasks represent different things. The two data sets may potentially be more accurately compared by extracting information from the confusion matrices of population decoder performance. For example, when the stimulus was at -30 deg, how often did the decoder classify the stimulus to a lefthand azimuth? Likewise, when the stimulus was +30 deg, how often did the decoder classify the stimulus to a righthand azimuth?

The azimuth discrimination error reported by Lauer et al. (2011) comes from engaged and highly trained mice, which is a very different context to our experimental setting with untrained mice passively listening to stimuli from 13 random azimuths. Therefore we did not perform analyses or interpretations of our results based on the behavioral task from Lauer et al. (2011) and only made the qualitative observation that the errors match for discussion.

We believe it is further important to clarify that Lauer et al. (2011) tested the ability of mice to discriminate between a positively conditioned stimulus (reference speaker at 0º center azimuth associated to a liquid reward) and a negatively conditioned stimulus (coming from one of five comparison speakers positioned at 20º, 30º, 50º, 70 and 90º azimuth, associated to an electrified lickport) in a conditioned avoidance task. In this task, mice are not precisely “discriminating between left or right sources centered around zero degrees”, making further analyses to compare the experimental design of Lauer et al (2011) and ours even more challenging for valid interpretation.

(4) DCIC can encode sound source azimuth in a similar format to that in the central nucleusof the inferior colliculus - It is unclear what exactly the authors mean by this statement in the Abstract. There are major differences in the encoding of azimuth between the two neighboring brain areas: a large majority of neurons in the CNIC are sensitive to azimuth (and strongly so), whereas the present study shows a minority of azimuth-sensitive neurons in the DCIC. Furthermore, CNIC neurons fire reliably to sound stimuli (low neural noise), whereas the present study shows that DCIC neurons fire more erratically (high neural noise).

Since sound source azimuth is reported to be encoded by population activity patterns at CNIC (Day and Delgutte, 2013), we refer to a population activity pattern code as the “similar format” in which this information is encoded at DCIC. Please note that this is a qualitative comparison and we do not claim this is the “same format”, due to the differences the reviewer precisely describes in the encoding of azimuth at CNIC where a much larger majority of neurons show stronger azimuth sensitivity and response reliability with respect to our observations at DCIC. By this qualitative similarity of encoding format we specifically mean the similar occurrence of activity patterns from azimuth sensitive subpopulations of neurons in both CNIC and DCIC, which carry sufficient information about the stimulus azimuth for a sufficiently accurate prediction with regard to the behavioral discrimination ability.

(5) Evidence of noise correlation between pairs of neurons exists - The authors' data andanalyses seem appropriate and sufficient to justify this claim.

(6) Noise correlations between responses of neurons help reduce population decodingerror - The authors show convincing analysis that performance of their decoder increased when simultaneously measured responses were tested (which include noise correlation) than when scrambled-trial responses were tested (eliminating noise correlation). This makes it seem likely that noise correlation in the responses improved decoder performance. The authors mention that the naïve Bayesian classifier was used as their decoder for computational efficiency, presumably because it assumes no noise correlation and, therefore, assumes responses of individual neurons are independent of each other across trials to the same stimulus. The use of decoder that assumes independence seems key here in testing the hypothesis that noise correlation contains information about sound source azimuth. The logic of using this decoder could be more clearly spelled out to the reader. For example, if the null hypothesis is that noise correlations do not carry azimuth information, then a decoder that assumes independence should perform the same whether population responses are simultaneous or scrambled. The authors' analysis showing a difference in performance between these two cases provides evidence against this null hypothesis.

We sincerely thank the reviewer for this careful and detailed consideration of our analysis approach. Following the reviewer’s constructive suggestion, we justified the decoder choice in the results section at the last paragraph of page 18:

“To characterize how the observed positive noise correlations could affect the representation of stimulus azimuth by DCIC top ranked unit population responses, we compared the decoding performance obtained by classifying the single-trial response patterns from top ranked units in the modeled decorrelated datasets versus the acquired data (with noise correlations). With the intention to characterize this with a conservative approach that would be less likely to find a contribution of noise correlations as it assumes response independence, we relied on the naive Bayes classifier for decoding throughout the study. Using this classifier, we observed that the modeled decorrelated datasets produced stimulus azimuth prediction error distributions that were significantly shifted towards higher decoding errors (Fig. 5B, C) and, in our imaging datasets, were not significantly different from chance level (Fig. 5B). Altogether, these results suggest that the detected noise correlations in our simultaneously acquired datasets can help reduce the error of the IC population code for sound azimuth.”

Minor weakness:

- Most studies of neural encoding of sound source azimuth are done in a noise-free environment, but the experimental setup in the present study had substantial background noise. This complicates comparison of the azimuth tuning results in this study to those of other studies. One is left wondering if azimuth sensitivity would have been greater in the absence of background noise, particularly for the imaging data where the signal was only about 12 dB above the noise. The description of the noise level and signal + noise level in the Methods should be made clearer. Mice hear from about 2.5 - 80 kHz, so it is important to know the noise level within this band as well as specifically within the band overlapping with the signal.

We agree with the reviewer that this information is useful. In our study, the background R.M.S. SPL during imaging across the mouse hearing range (2.5-80kHz) was 44.53 dB and for neuropixels recordings 34.68 dB. We have added this information to the methods section of the revised manuscript.

Reviewer #2 (Public Review):

In the present study, Boffi et al. investigate the manner in which the dorsal cortex of the of the inferior colliculus (DCIC), an auditory midbrain area, encodes sound location azimuth in awake, passively listening mice. By employing volumetric calcium imaging (scanned temporal focusing or s-TeFo), complemented with high-density electrode electrophysiological recordings (neuropixels probes), they show that sound-evoked responses are exquisitely noisy, with only a small portion of neurons (units) exhibiting spatial sensitivity. Nevertheless, a naïve Bayesian classifier was able to predict the presented azimuth based on the responses from small populations of these spatially sensitive units. A portion of the spatial information was provided by correlated trial-to-trial response variability between individual units (noise correlations). The study presents a novel characterization of spatial auditory coding in a non-canonical structure, representing a noteworthy contribution specifically to the auditory field and generally to systems neuroscience, due to its implementation of state-of-the-art techniques in an experimentally challenging brain region. However, nuances in the calcium imaging dataset and the naïve Bayesian classifier warrant caution when interpreting some of the results.

Strengths:

The primary strength of the study lies in its methodological achievements, which allowed the authors to collect a comprehensive and novel dataset. While the DCIC is a dorsal structure, it extends up to a millimetre in depth, making it optically challenging to access in its entirety. It is also more highly myelinated and vascularised compared to e.g., the cerebral cortex, compounding the problem. The authors successfully overcame these challenges and present an impressive volumetric calcium imaging dataset. Furthermore, they corroborated this dataset with electrophysiological recordings, which produced overlapping results. This methodological combination ameliorates the natural concerns that arise from inferring neuronal activity from calcium signals alone, which are in essence an indirect measurement thereof.

Another strength of the study is its interdisciplinary relevance. For the auditory field, it represents a significant contribution to the question of how auditory space is represented in the mammalian brain. "Space" per se is not mapped onto the basilar membrane of the cochlea and must be computed entirely within the brain. For azimuth, this requires the comparison between miniscule differences between the timing and intensity of sounds arriving at each ear. It is now generally thought that azimuth is initially encoded in two, opposing hemispheric channels, but the extent to which this initial arrangement is maintained throughout the auditory system remains an open question. The authors observe only a slight contralateral bias in their data, suggesting that sound source azimuth in the DCIC is encoded in a more nuanced manner compared to earlier processing stages of the auditory hindbrain. This is interesting, because it is also known to be an auditory structure to receive more descending inputs from the cortex.

Systems neuroscience continues to strive for the perfection of imaging novel, less accessible brain regions. Volumetric calcium imaging is a promising emerging technique, allowing the simultaneous measurement of large populations of neurons in three dimensions. But this necessitates corroboration with other methods, such as electrophysiological recordings, which the authors achieve. The dataset moreover highlights the distinctive characteristics of neuronal auditory representations in the brain. Its signals can be exceptionally sparse and noisy, which provide an additional layer of complexity in the processing and analysis of such datasets. This will be undoubtedly useful for future studies of other less accessible structures with sparse responsiveness.

Weaknesses:

Although the primary finding that small populations of neurons carry enough spatial information for a naïve Bayesian classifier to reasonably decode the presented stimulus is not called into question, certain idiosyncrasies, in particular the calcium imaging dataset and model, complicate specific interpretations of the model output, and the readership is urged to interpret these aspects of the study's conclusions with caution.

I remain in favour of volumetric calcium imaging as a suitable technique for the study, but the presently constrained spatial resolution is insufficient to unequivocally identify regions of interest as cell bodies (and are instead referred to as "units" akin to those of electrophysiological recordings). It remains possible that the imaging set is inadvertently influenced by non-somatic structures (including neuropil), which could report neuronal activity differently than cell bodies. Due to the lack of a comprehensive ground-truth comparison in this regard (which to my knowledge is impossible to achieve with current technology), it is difficult to imagine how many informative such units might have been missed because their signals were influenced by spurious, non-somatic signals, which could have subsequently misled the models. The authors reference the original Nature Methods article (Prevedel et al., 2016) throughout the manuscript, presumably in order to avoid having to repeat previously published experimental metrics. But the DCIC is neither the cortex nor hippocampus (for which the method was originally developed) and may not have the same light scattering properties (not to mention neuronal noise levels). Although the corroborative electrophysiology data largely eleviates these concerns for this particular study, the readership should be cognisant of such caveats, in particular those who are interested in implementing the technique for their own research.

A related technical limitation of the calcium imaging dataset is the relatively low number of trials (14) given the inherently high level of noise (both neuronal and imaging). Volumetric calcium imaging, while offering a uniquely expansive field of view, requires relatively high average excitation laser power (in this case nearly 200 mW), a level of exposure the authors may have wanted to minimise by maintaining a low the number of repetitions, but I yield to them to explain.

We assumed that the levels of heating by excitation light measured at the neocortex in Prevedel et al. (2016), were representative for DCIC also. Nevertheless, we recognize this approximation might not be very accurate, due to the differences in tissue architecture and vascularization from these two brain areas, just to name a few factors. The limiting factor preventing us from collecting more trials in our imaging sessions was that we observed signs of discomfort or slight distress in some mice after ~30 min of imaging in our custom setup, which we established as a humane end point to prevent distress. In consequence imaging sessions were kept to 25 min in duration, limiting the number of trials collected. However we cannot rule out that with more extensive habituation prior to experiments the imaging sessions could be prolonged without these signs of discomfort or if indeed influence from our custom setup like potential heating of the brain by illumination light might be the causing factor of the observed distress. Nevertheless, we note that previous work has shown that ~200mW average power is a safe regime for imaging in the cortex by keeping brain heating minimal (Prevedel et al., 2016), without producing the lasting damages observed by immunohistochemisty against apoptosis markers above 250mW (Podgorski and Ranganathan 2016, https://doi.org/10.1152/jn.00275.2016).

Calcium imaging is also inherently slow, requiring relatively long inter-stimulus intervals (in this case 5 s). This unfortunately renders any model designed to predict a stimulus (in this case sound azimuth) from particularly noisy population neuronal data like these as highly prone to overfitting, to which the authors correctly admit after a model trained on the entire raw dataset failed to perform significantly above chance level. This prompted them to feed the model only with data from neurons with the highest spatial sensitivity. This ultimately produced reasonable performance (and was implemented throughout the rest of the study), but it remains possible that if the model was fed with more repetitions of imaging data, its performance would have been more stable across the number of units used to train it. (All models trained with imaging data eventually failed to converge.) However, I also see these limitations as an opportunity to improve the technology further, which I reiterate will be generally important for volume imaging of other sparse or noisy calcium signals in the brain.

Transitioning to the naïve Bayesian classifier itself, I first openly ask the authors to justify their choice of this specific model. There are countless types of classifiers for these data, each with their own pros and cons. Did they actually try other models (such as support vector machines), which ultimately failed? If so, these negative results (even if mentioned en passant) would be extremely valuable to the community, in my view. I ask this specifically because different methods assume correspondingly different statistical properties of the input data, and to my knowledge naïve Bayesian classifiers assume that predictors (neuronal responses) are assumed to be independent within a class (azimuth). As the authors show that noise correlations are informative in predicting azimuth, I wonder why they chose a model that doesn't take advantage of these statistical regularities. It could be because of technical considerations (they mention computing efficiency), but I am left generally uncertain about the specific logic that was used to guide the authors through their analytical journey.

One of the main reasons we chose the naïve Bayesian classifier is indeed because it assumes that the responses of the simultaneously recorded neurons are independent and therefore it does not assume a contribution of noise correlations to the estimation of the posterior probability of each azimuth. This model would represent the null hypothesis that noise correlations do not contribute to the encoding of stimulus azimuth, which would be verified by an equal decoding outcome from correlated or decorrelated datasets. Since we observed that this is not the case, the model supports the alternative hypothesis that noise correlations do indeed influence stimulus azimuth encoding. We wanted to test these hypotheses with the most conservative approach possible that would be least likely to find a contribution of noise correlations. Other relevant reasons that justify our choice of the naive Bayesian classifier are its robustness against the limited numbers of trials we could collect in comparison to other more “data hungry” classifiers like SVM, KNN, or artificial neuronal nets. We did perform preliminary tests with alternative classifiers but the obtained decoding errors were similar when decoding the whole population activity (Author response image 2A). Dimensionality reduction following the approach described in the manuscript showed a tendency towards smaller decoding errors observed with an alternative classifier like KNN, but these errors were still larger than the ones observed with the naive Bayesian classifier (median error 45º). Nevertheless, we also observe a similar tendency for slightly larger decoding errors in the absence of noise correlations (decorrelated, Author response image 2B). Sentences detailing the logic of classifier choice are now included in the results section at page 10 and at the last paragraph of page 18 (see responses to Reviewer 1).

Author response image 2.

A) Cumulative distribution plots of the absolute cross-validated single-trial prediction errors obtained using different classifiers (blue; KNN: K-nearest neighbors; SVM: support vector machine ensemble) and chance level distribution (gray) on the complete populations of imaged units. Cumulative distribution plots of the absolute cross-validated singletrial prediction errors obtained using a Bayes classifier (naive approximation for computation efficiency) to decode the single-trial response patterns from the 31 top ranked units in the simultaneously imaged datasets across mice (cyan), modeled decorrelated datasets (orange) and the chance level distribution associated with our stimulation paradigm (gray). Vertical dashed lines show the medians of cumulative distributions. K.S. w/Sidak: Kolmogorov-Smirnov with Sidak.

That aside, there remain other peculiarities in model performance that warrant further investigation. For example, what spurious features (or lack of informative features) in these additional units prevented the models of imaging data from converging?

Considering the amount of variability observed throughout the neuronal responses both in imaging and neuropixels datasets, it is easy to suspect that the information about stimulus azimuth carried in different amounts by individual DCIC neurons can be mixed up with information about other factors (Stringer et al., 2019). In an attempt to study the origin of these features that could confound stimulus azimuth decoding we explored their relation to face movement (Supplemental Figure 2), finding a correlation to snout movements, in line with previous work by Stringer et al. (2019).

In an orthogonal question, did the most spatially sensitive units share any detectable tuning features? A different model trained with electrophysiology data in contrast did not collapse in the range of top-ranked units plotted. Did this model collapse at some point after adding enough units, and how well did that correlate with the model for the imaging data?

Our electrophysiology datasets were much smaller in size (number of simultaneously recorded neurons) compared to our volumetric calcium imaging datasets, resulting in a much smaller total number of top ranked units detected per dataset. This precluded the determination of a collapse of decoder performance due to overfitting beyond the range plotted in Fig 4G.

How well did the form (and diversity) of the spatial tuning functions as recorded with electrophysiology resemble their calcium imaging counterparts? These fundamental questions could be addressed with more basic, but transparent analyses of the data (e.g., the diversity of spatial tuning functions of their recorded units across the population). Even if the model extracts features that are not obvious to the human eye in traditional visualisations, I would still find this interesting.

The diversity of the azimuth tuning curves recorded with calcium imaging (Fig. 3B) was qualitatively larger than the ones recorded with electrophysiology (Fig. 4B), potentially due to the larger sampling obtained with volumetric imaging. We did not perform a detailed comparison of the form and a more quantitative comparison of the diversity of these functions because the signals compared are quite different, as calcium indicator signal is subject to non linearities due to Ca2+ binding cooperativity and low pass filtering due to binding kinetics. We feared this could lead to misleading interpretations about the similarities or differences between the azimuth tuning functions in imaged and electrophysiology datasets. Our model uses statistical response dependency to stimulus azimuth, which does not rely on features from a descriptive statistic like mean response tuning. In this context, visualizing the trial-to-trial responses as a function of azimuth shows “features that are not obvious to the human eye in traditional visualizations” (Fig. 3D, left inset).

Finally, the readership is encouraged to interpret certain statements by the authors in the current version conservatively. How the brain ultimately extracts spatial neuronal data for perception is anyone's guess, but it is important to remember that this study only shows that a naïve Bayesian classifier could decode this information, and it remains entirely unclear whether the brain does this as well. For example, the model is able to achieve a prediction error that corresponds to the psychophysical threshold in mice performing a discrimination task (~30 {degree sign}). Although this is an interesting coincidental observation, it does not mean that the two metrics are necessarily related. The authors correctly do not explicitly claim this, but the manner in which the prose flows may lead a non-expert into drawing that conclusion.

To avoid misleading the non-expert readers, we have clarified in the manuscript that the observed correspondence between decoding error and psychophysical threshold is explicitly coincidental.

Page 13, end of middle paragraph:

“If we consider the median of the prediction error distribution as an overall measure of decoding performance, the single-trial response patterns from subsamples of at least the 7 top ranked units produced median decoding errors that coincidentally matched the reported azimuth discrimination ability of mice (Fig 4G, minimum audible angle = 31º) (Lauer et al., 2011).”

Page 14, bottom paragraph:

“Decoding analysis (Fig. 4F) of the population response patterns from azimuth dependent top ranked units simultaneously recorded with neuropixels probes showed that the 4 top ranked units are the smallest subsample necessary to produce a significant decoding performance that coincidentally matches the discrimination ability of mice (31° (Lauer et al., 2011)) (Fig. 5F, G).”

We also added to the Discussion sentences clarifying that a relationship between these two variables remains to be determined and it also remains to be determined if the DCIC indeed performs a bayesian decoding computation for sound localization.

Page 20, bottom:

“… Concretely, we show that sound location coding does indeed occur at DCIC on the single trial basis, and that this follows a comparable mechanism to the characterized population code at CNIC (Day and Delgutte, 2013). However, it remains to be determined if indeed the DCIC network is physiologically capable of Bayesian decoding computations. Interestingly, the small number of DCIC top ranked units necessary to effectively decode stimulus azimuth suggests that sound azimuth information is redundantly distributed across DCIC top ranked units, which points out that mechanisms beyond coding efficiency could be relevant for this population code.

While the decoding error observed from our DCIC datasets obtained in passively listening, untrained mice coincidentally matches the discrimination ability of highly trained, motivated mice (Lauer et al., 2011), a relationship between decoding error and psychophysical performance remains to be determined. Interestingly, a primary sensory representations should theoretically be even more precise than the behavioral performance as reported in the visual system (Stringer et al., 2021).”

Moreover, the concept of redundancy (of spatial information carried by units throughout the DCIC) is difficult for me to disentangle. One interpretation of this formulation could be that there are non-overlapping populations of neurons distributed across the DCIC that each could predict azimuth independently of each other, which is unlikely what the authors meant. If the authors meant generally that multiple neurons in the DCIC carry sufficient spatial information, then a single neuron would have been able to predict sound source azimuth, which was not the case. I have the feeling that they actually mean "complimentary", but I leave it to the authors to clarify my confusion, should they wish.

We observed that the response patterns from relatively small fractions of the azimuth sensitive DCIC units (4-7 top ranked units) are sufficient to generate an effective code for sound azimuth, while 32-40% of all simultaneously recorded DCIC units are azimuth sensitive. In light of this observation, we interpreted that the azimuth information carried by the population should be redundantly distributed across the complete subpopulation of azimuth sensitive DCIC units.

In summary, the present study represents a significant body of work that contributes substantially to the field of spatial auditory coding and systems neuroscience. However, limitations of the imaging dataset and model as applied in the study muddles concrete conclusions about how the DCIC precisely encodes sound source azimuth and even more so to sound localisation in a behaving animal. Nevertheless, it presents a novel and unique dataset, which, regardless of secondary interpretation, corroborates the general notion that auditory space is encoded in an extraordinarily complex manner in the mammalian brain.

Reviewer #3 (Public Review):

Summary:

Boffi and colleagues sought to quantify the single-trial, azimuthal information in the dorsal cortex of the inferior colliculus (DCIC), a relatively understudied subnucleus of the auditory midbrain. They used two complementary recording methods while mice passively listened to sounds at different locations: a large volume but slow sampling calcium-imaging method, and a smaller volume but temporally precise electrophysiology method. They found that neurons in the DCIC were variable in their activity, unreliably responding to sound presentation and responding during inter-sound intervals. Boffi and colleagues used a naïve Bayesian decoder to determine if the DCIC population encoded sound location on a single trial. The decoder failed to classify sound location better than chance when using the raw single-trial population response but performed significantly better than chance when using intermediate principal components of the population response. In line with this, when the most azimuth dependent neurons were used to decode azimuthal position, the decoder performed equivalently to the azimuthal localization abilities of mice. The top azimuthal units were not clustered in the DCIC, possessed a contralateral bias in response, and were correlated in their variability (e.g., positive noise correlations). Interestingly, when these noise correlations were perturbed by inter-trial shuffling decoding performance decreased. Although Boffi and colleagues display that azimuthal information can be extracted from DCIC responses, it remains unclear to what degree this information is used and what role noise correlations play in azimuthal encoding.

Strengths:

The authors should be commended for collection of this dataset. When done in isolation (which is typical), calcium imaging and linear array recordings have intrinsic weaknesses. However, those weaknesses are alleviated when done in conjunction with one another - especially when the data largely recapitulates the findings of the other recording methodology. In addition to the video of the head during the calcium imaging, this data set is extremely rich and will be of use to those interested in the information available in the DCIC, an understudied but likely important subnucleus in the auditory midbrain.

The DCIC neural responses are complex; the units unreliably respond to sound onset, and at the very least respond to some unknown input or internal state (e.g., large inter-sound interval responses). The authors do a decent job in wrangling these complex responses: using interpretable decoders to extract information available from population responses.

Weaknesses:

The authors observe that neurons with the most azimuthal sensitivity within the DCIC are positively correlated, but they use a Naïve Bayesian decoder which assume independence between units. Although this is a bit strange given their observation that some of the recorded units are correlated, it is unlikely to be a critical flaw. At one point the authors reduce the dimensionality of their data through PCA and use the loadings onto these components in their decoder. PCA incorporates the correlational structure when finding the principal components and constrains these components to be orthogonal and uncorrelated. This should alleviate some of the concern regarding the use of the naïve Bayesian decoder because the projections onto the different components are independent. Nevertheless, the decoding results are a bit strange, likely because there is not much linearly decodable azimuth information in the DCIC responses. Raw population responses failed to provide sufficient information concerning azimuth for the decoder to perform better than chance. Additionally, it only performed better than chance when certain principal components or top ranked units contributed to the decoder but not as more components or units were added. So, although there does appear to be some azimuthal information in the recoded DCIC populations - it is somewhat difficult to extract and likely not an 'effective' encoding of sound localization as their title suggests.

As described in the responses to reviewers 1 and 2, we chose the naïve Bayes classifier as a decoder to determine the influence of noise correlations through the most conservative approach possible, as this classifier would be least likely to find a contribution of correlated noise. Also, we chose this decoder due to its robustness against limited numbers of trials collected, in comparison to “data hungry” non linear classifiers like KNN or artificial neuronal nets. Lastly, we observed that small populations of noisy, unreliable (do not respond in every trial) DCIC neurons can encode stimulus azimuth in passively listening mice matching the discrimination error of trained mice. Therefore, while this encoding is definitely not efficient, it can still be considered effective.

Although this is quite a worthwhile dataset, the authors present relatively little about the characteristics of the units they've recorded. This may be due to the high variance in responses seen in their population. Nevertheless, the authors note that units do not respond on every trial but do not report what percent of trials that fail to evoke a response. Is it that neurons are noisy because they do not respond on every trial or is it also that when they do respond they have variable response distributions? It would be nice to gain some insight into the heterogeneity of the responses.

The limited number of azimuth trial repetitions that we could collect precluded us from making any quantification of the unreliability (failures to respond) and variability in the response distributions from the units we recorded, as we feared they could be misleading. In qualitative terms, “due to the high variance in responses seen” in the recordings and the limited trial sampling, it is hard to make any generalization. In consequence we referred to the observed response variance altogether as neuronal noise. Considering these points, our datasets are publicly available for exploration of the response characteristics.

Additionally, is there any clustering at all in response profiles or is each neuron they recorded in the DCIC unique?

We attempted to qualitatively visualize response clustering using dimensionality reduction, observing different degrees of clustering or lack thereof across the azimuth classes in the datasets collected from different mice. It is likely that the limited number of azimuth trials we could collect and the high response variance contribute to an inconsistent response clustering across datasets.

They also only report the noise correlations for their top ranked units, but it is possible that the noise correlations in the rest of the population are different.

For this study, since our aim was to interrogate the influence of noise correlations on stimulus azimuth encoding by DCIC populations, we focused on the noise correlations from the top ranked unit subpopulation, which likely carry the bulk of the sound location information.  Noise correlations can be defined as correlation in the trial to trial response variation of neurons. In this respect, it is hard to ascertain if the rest of the population, that is not in the top rank unit percentage, are really responding and showing response variation to evaluate this correlation, or are simply not responding at all and show unrelated activity altogether. This makes observations about noise correlations from “the rest of the population” potentially hard to interpret.

It would also be worth digging into the noise correlations more - are units positively correlated because they respond together (e.g., if unit x responds on trial 1 so does unit y) or are they also modulated around their mean rates on similar trials (e.g., unit x and y respond and both are responding more than their mean response rate). A large portion of trial with no response can occlude noise correlations. More transparency around the response properties of these populations would be welcome.

Due to the limited number of azimuth trial repetitions collected, to evaluate noise correlations we used the non parametric Kendall tau correlation coefficient which is a measure of pairwise rank correlation or ordinal association in the responses to each azimuth. Positive rank correlation would represent neurons more likely responding together. Evaluating response modulation “around their mean rates on similar trials” would require assumptions about the response distributions, which we avoided due to the potential biases associated with limited sample sizes.

It is largely unclear what the DCIC is encoding. Although the authors are interested in azimuth, sound location seems to be only a small part of DCIC responses. The authors report responses during inter-sound interval and unreliable sound-evoked responses. Although they have video of the head during recording, we only see a correlation to snout and ear movements (which are peculiar since in the example shown it seems the head movements predict the sound presentation). Additional correlates could be eye movements or pupil size. Eye movement are of particular interest due to their known interaction with IC responses - especially if the DCIC encodes sound location in relation to eye position instead of head position (though much of eye-position-IC work was done in primates and not rodent). Alternatively, much of the population may only encode sound location if an animal is engaged in a localization task. Ideally, the authors could perform more substantive analyses to determine if this population is truly noisy or if the DCIC is integrating un-analyzed signals.

We unsuccessfully attempted eye tracking and pupillometry in our videos. We suspect that the reason behind this is a generally overly dilated pupil due to the low visible light illumination conditions we used which were necessary to protect the PMT of our custom scope.

It is likely that DCIC population activity is integrating un-analyzed signals, like the signal associated with spontaneous behaviors including face movements (Stringer et al., 2019), which we observed at the level of spontaneous snout movements. However investigating if and how these signals are integrated to stimulus azimuth coding requires extensive behavioral testing and experimentation which is out of the scope of this study. For the purpose of our study, we referred to trial-to-trial response variation as neuronal noise. We note that this definition of neuronal noise can, and likely does, include an influence from un-analyzed signals like the ones from spontaneous behaviors.

Although this critique is ubiquitous among decoding papers in the absence of behavioral or causal perturbations, it is unclear what - if any - role the decoded information may play in neuronal computations. The interpretation of the decoder means that there is some extractable information concerning sound azimuth - but not if it is functional. This information may just be epiphenomenal, leaking in from inputs, and not used in computation or relayed to downstream structures. This should be kept in mind when the authors suggest their findings implicate the DCIC functionally in sound localization.

Our study builds upon previous reports by other independent groups relying on “causal and behavioral perturbations” and implicating DCIC in sound location learning induced experience dependent plasticity (Bajo et al., 2019, 2010; Bajo and King, 2012), which altogether argues in favor of DCIC functionality in sound localization.

Nevertheless, we clarified in the discussion of the revised manuscript that a relationship between the observed decoding error and the psychophysical performance, or the ability of the DCIC network to perform Bayesian decoding computations, both remain to be determined (please see responses to Reviewer #2).

It is unclear why positive noise correlations amongst similarly tuned neurons would improve decoding. A toy model exploring how positive noise correlations in conjunction with unreliable units that inconsistently respond may anchor these findings in an interpretable way. It seems plausible that inconsistent responses would benefit from strong noise correlations, simply by units responding together. This would predict that shuffling would impair performance because you would then be sampling from trials in which some units respond, and trials in which some units do not respond - and may predict a bimodal performance distribution in which some trials decode well (when the units respond) and poor performance (when the units do not respond).

In samples with more that 2 dimensions, the relationship between signal and noise correlations is more complex than in two dimensional samples (Montijn et al., 2016) which makes constructing interpretable and simple toy models of this challenging. Montijn et al. (2016) provide a detailed characterization and model describing how the accuracy of a multidimensional population code can improve when including “positive noise correlations amongst similarly tuned neurons”. Unfortunately we could not successfully test their model based on Mahalanobis distances as we could not verify that the recorded DCIC population responses followed a multivariate gaussian distribution, due to the limited azimuth trial repetitions we could sample.

Significance:

Boffi and colleagues set out to parse the azimuthal information available in the DCIC on a single trial. They largely accomplish this goal and are able to extract this information when allowing the units that contain more information about sound location to contribute to their decoding (e.g., through PCA or decoding on top unit activity specifically). The dataset will be of value to those interested in the DCIC and also to anyone interested in the role of noise correlations in population coding. Although this work is first step into parsing the information available in the DCIC, it remains difficult to interpret if/how this azimuthal information is used in localization behaviors of engaged mice.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

General:

The manuscript is generally well written, but could benefit from a quick proof by a native English speaker (e.g., "the" inferior colliculus is conventionally used with its article). The flow of arguments is also generally easy to follow, but I would kindly ask the authors to consider elaborating or clarifying the following points (including those already mentioned in my public review).

(1) Choice of model:

There are countless ways one can construct a decoder or classifier that can predict a presented sensory stimulus based on a population neuronal response. Given the assumptions of independence as mentioned in my public review, I would ask the authors to explicitly justify their choice of a naïve Bayesian classifier.

A section detailing the logic of classifier choice is now included in the results section at page 10 and the last paragraph of page 18 from the revised version of the manuscript.

(2) Number of imaging repetitions:

For particularly noisy datasets, 14 repetitions is indeed quite few. I reckon this was not the choice of the authors, but rather limited by the inherent experimental conditions. Despite minimisation of required average laser power during the development of s-TeFo imaging, the authors still required almost 200 mW (which is still quite a lot of exposure). Although 14 repetitions for 13 azimuthal locations every 5 s is at face value a relatively short imaging session (~15 min.), at 191 mW, with the desire to image mice multiple times, I could imagine that this is a practical limitation the authors faced (to avoid excessive tissue heating or photodamage, which was assessed in the original Nature Methods article, but not here). Nevertheless, this logic (or whatever logic they had) should be explained for non-imaging experts in the readership.

This is now addressed in the answers to the public reviews.

(3) Redundancy:

It is honestly unclear to me what the authors mean by this. I don't speculate that they mean there are "redundant" (small) populations of neurons that sufficiently encode azimuth, but I'm actually not certain. If that were the case, I believe this would need further clarification, since redundant representations would be both inconsistent with the general (perhaps surprising) finding that large populations are not required in the DCIC, which is thought to be the case at earlier processing stages.

In the text we are referring to the azimuth information being redundantly distributed across DCIC top ranked units. We do not mention redundant “populations of neurons”.

(4) Correspondence of decoding accuracy with psychometric functions in mice: While this is an interesting coincidental observation, it should not be interpreted that the neuronal detection threshold in the DCIC somehow is somehow responsible its psychometric counterpart (which is an interesting yet exceedingly complex question). Although I do not believe the authors intended to suggest this, I would personally be cautious in the way I describe this correspondence. I mention this because the authors point it out multiple times in the manuscript (whereas I would have just mentioned it once in passing).

This is now clarified in the revised manuscript.

(5) Noisy vs. sparse:

I'm confident that the authors understand the differences between these terms, both in concept (stochastic vs. scattered) and in context (neuronal vs. experimental), but I personally would be cautious in the way I use them in the description of the study. Indeed, auditory neuronal signals are to my knowledge generally thought to be both sparse and noisy, which is in itself interesting, but the study also deals with substantial experimental (recording) noise, and I think it's important for the readership to understand when "noise" refers to the recordings (in particular the imaging data) and to neuronal activity. I mention this specifically because "noisy" appears in the title.

We have clarified this issue at the bottom of page 5 by adding the following sentences to the revised manuscript:

“In this section we used the word “noise” to refer to the sound stimuli used and recording setup background sound levels or recording noise in the acquired signals. To avoid confusion, from now on in the manuscript the word “noise” will be used in the context of neuronal noise, which is the trial-to-trial variation in neuronal responses unrelated to stimuli, unless otherwise noted.”

(6)  More details in the Methods:

The Methods section is perhaps the least-well structured part of the present manuscript in my view, and I encourage the authors to carefully go through it and add the following information (in case I somehow missed it).

a. Please also indicate the number of animals used here.

Added.

b. How many sessions were performed on each mouse?

This is already specified in the methods section in page 25:

“mice were imaged a total of 2-11 times (sessions), one to three times a week.”

We added for clarification:

“Datasets here analyzed and reported come from the imaging session in which we observed maximal calcium sensor signal (peak AAV expression) and maximum number of detected units.”

c. For the imaging experiments, was it possible to image the same units from session tosession?

This is not possible for sTeFo 2P data due to low spatial resolution which makes precisely matching neuron ROIs across sessions challenging.

d. Could the authors please add more detail to the analyses of the videos (to track facialmovements) or provide a reference?

Added citation.

e. The same goes for the selection of subcellular regions of interest that were used as"units."

Added to page 25:

“We used the CaImAn package (Giovannucci et al., 2019) for automatic ROI segmentation through constrained non negative matrix factorization and selected ROIs (Units) showing clear Ca transients consistent with neuronal activity, and IC neuron somatic shape and size (Schofield and Beebe, 2019).”

Specific: In order to maximise the efficiency of my comments and suggestions (as there are no line numbers), my numerated points are organised in sequential order.

(1) Abstract: I wouldn't personally motivate the study with the central nucleus of the IC (i.e. Idon't think this is necessary). I think the authors can motivate it simply with the knowledge gaps in spatial coding throughout the auditory system, in which such large data sets such as the ones presented here are of general value.

(2) Page 4: 15-50 kHz "white" noise is incorrect. It should be "band-passed" noise.

Changed.

(3) Supplemental figure 1, panel A: Since the authors could not identify cell bodiesunequivocally from their averaged volume timeseries data, it would be clearer to the readership if larger images are shown, so that they can evaluate (speculate) for themselves what subcellular structures were identified as units. Even better would be to include a planar image through a cross-section. As mentioned above, not everything determined for the cortex or hippocampus can be assumed to be true for the DCIC.

The raw images and segmentations are publicly available for detailed inspections.

(4) Supplemental figure 2, panel A: This panel requires further explanation, in particular thepanel on the right. I assume that to be a simple subtraction of sequential frames, but I'm thrown off by the "d(Grey)" colour bar. Also, if "grey" refers to the neutral colour, it is conventionally spelled "gray" in US-American English.

Changed.

(5) Supplemental figure 2, panel B: I'm personally curious why the animals exhibitedmovement just prior to a stimulus. Did they learn to anticipate the presentation of a sound after some habituation? Is that somehow a pre-emptive startle response? We observe that in our own experiments (but as we stochastically vary the inter-trial-intervals, the movement typically occurs directly after the stimulus). I don't suggest the authors dwell on this, but I find it an interesting observation.

It is indeed interesting, but we can’t conclude much about it without comparing it to random inter-trial-intervals.

(6) Supplemental figure 3: I personally find these data (decoding of all electrophysiologicaldata) of central relevance to the study, since it mirrors the analyses presented for its imaging data counterpart and encourage the authors to move it to the main text.

Changed.

(7) Page 12: Do the authors have any further analyses of spatial tuning functions? We allknow they can parametrically obscure (i.e., bi-lobed, non-monotonic, etc.), but having these parameters (even if just in a supplemental figure) would be informative for the spatial auditory community.

We dedicated significant effort to attempt to parametrize and classify the azimuth response dependency functions from the recorded DCIC cells in an unbiased way. Nevertheless, given the observed response noise and the “obscure” properties of spatial tuning functions mentioned by the reviewer, we could only reach the general qualitative observation of having a more frequent contralateral selectivity.

(8) Page 14 (end): Here, psychometric correspondence is referenced. Please add theLauer et al., (2011) reference, or, as I would, remove the statement entirely and save it for the discussion (where it is also mentioned and referenced).

Changed.

(9) Figure 5, Panels B and C: Why don't the authors report the Kruskal-Wallis tests (forincreasing number of units training the model), akin to e.g., Panel G of Figure 4? I think that would be interesting to see (e.g., if the number of required units to achieve statistical significance is the same).

Within class randomization produced a moderate effect on decoder performance, achieving statistical significance at similar numbers of units, as seen in figure 5 panels B and C. We did not include these plots for the sake of not cluttering the figure with dense distributions and fuzzing the visualization of the differences between the distributions shown.

(10) Figure 5, Panels B and C (histograms): I see a bit of skewedness in the distributions(even after randomisation). Where does this come from? This is just a small talking point.

We believe this is potentially due to more than one distribution of pairwise correlations combined into one histogram (like in a Gaussian mixture model).

(11) Page 21: Could the authors please specify that the Day and Delgutte (2013) study wasperformed on rabbits? Since rabbits have an entirely different spectral hearing range compared to mice, spatial coding principles could very well be different in those animals (and I'm fairly certain such a study has not yet been published for mice).

Specified.

(12) Page 22: I'd encourage the authors to remove the reference to Rayleigh's duplextheory, since mice hardly (if at all) use interaural time differences for azimuthal sound localisation, given their generally high-frequency hearing range.

That sentence is meant to discuss beyond the mouse model an exciting outlook of our findings in light of previous reports, which is a hypothetical functional relationship between the tonotopy in DCIC and the spatial distribution of azimuth sensitive DCIC neurons. We have clarified this now in the text.

(13) Page 23: I believe the conventional verb for gene delivery with viruses is still"transduce" (or "infect", but not "induce"). What was the specific "syringe" used for stereotactic injections? Also, why were mice housed separately after surgery? This question pertains to animal welfare.

Changed. The syringe was a 10ml syringe to generate positive or negative pressure, coupled to the glass needle through a silicon tubing via a luer 3-way T valve. Single housing was chosen to avoid mice compromising each other’s implantations. Therefore this can be seen as a refinement of our method to maximize the chances of successful imaging per implanted mouse.

(14) Page 25: Could the authors please indicate the refractory period violation time windowhere? I had to find it buried in the figure caption of Supplementary figure 1.

Added.

(15) Page 27: What version of MATLAB was used? This could be important for reproductionof the analyses, since The Mathworks is infamously known to add (or even more deplorably, modify) functions in particular versions (and not update older ones accordingly).

Added.

Reviewer #3 (Recommendations For The Authors):

Overall I thought this was a nice manuscript and a very interesting dataset. Here are some suggestions and minor corrections:

You may find this work of interest - 'A monotonic code for sound azimuth in primate inferior colliculus' 2003, Groh, Kelly & Underhill.

We thank the reviewer for pointing out this extremely relevant reference, which we regrettably failed to cite. It is now included in the revised version of the manuscript.

In your introduction, you state "our findings point to a functional role of DCIC in sound location coding". Though your results show that there is azimuthal information contained in a subset of DCIC units there's no evidence in the manuscript that shows a functional link between this representation and sound localization.

This is now addressed in the answers to the public reviews.

I found the variability in your DCIC population quite striking - especially during the intersound intervals. The entrainment of the population in the imaging datatset suggests some type of input activating the populations - maybe these are avenues for further probing the variability here:

(1) I'm curious if you can extract eye movements from your video. Work from Jennifer Grohshows that some cells in the primate inferior colliculus are sensitive to different eye positions (Groh et. al., 2001). With recent work showing eye movements in rodents, it may explain some of the variance in the DCIC responses.

This is now addressed in the answers to the public reviews.

(2) I was also curious if the motor that moves the speaker made noise It could be possiblesome of the 'on going' activity could be some sound-evoked response.

We were careful to set the stepper motor speed so that it produced low frequency noise, within a band mostly outside of the hearing range of mice (<4kHz). Nevertheless, we cannot fully rule out that a very quiet but perhaps very salient component of the motor noise could influence the activity during the inter trial periods. The motor was stationary and quiet for a period of at least one stimulus duration before and during stimulus presentation.  

(3) Was the sound you present frozen or randomly generated on each trial? Could therebe some type of structure in the noise you presented that sometimes led cells to respond to a particular azimuth location but not others?

The sound presented was frozen noise. This is now clarified in the methods section.

It may be useful to quantify the number of your units that had refractory period violations.

Our manual curation of sorted units was very stringent to avoid mixing differently tuned neurons. The single units analyzed had very infrequent refractory period violations, in less than ~5% of the spikes, considering a 2 ms refractory period.

Was the video recording contralateral or ipsilateral to the recording?

The side of the face ipsilateral to the imaged IC was recorded. Added to methods.

I was struck by the snout and ear movements - in the example shown in Supplementary Figure 2B it appears as they are almost predicting sound onset. Was there any difference in ear movements in the habituated and non-habituated animals? Also, does the placement of the cranial window disturb any of the muscles used in ear movement?

Mouse snout movements appear to be quite active perhaps reflecting arousal (Stringer et al., 2019). We cannot rule out that the cranial window implantation disturbed ear movement but while moving the mouse headfixed we observed what could be considered normal ear movements.

Did you correlate time-point by time-point in the average population activity and movement or did you try different temporal labs/leads in case the effect of the movements was delayed in some way?

Point by point due to 250ms time resolution of imaging.

Are the video recordings only available during the imaging? It would be nice to see the same type of correlations in the neuropixel-acquired data as well.

Only imaging. For neuropixels recordings, we were skeptical about face videography as we suspected that face movements were likely influenced by the acute nature of the preparation procedure. Our cranial window preparation in the other hand involved a recovery period of at least 4 weeks. Therefore we were inclined to perform videographical interrogation of face movements on these mice instead.

If you left out more than 1 trial do you think this would help your overfitting issue (e.g. leaving out 20% of the data).

Due to the relatively small number of trial repetitions collected, fitting the model with an even smaller training dataset is unlikely to help overfitting and will likely decrease decoder performance.

It would be nice to see a confusion matrix - even though azimuthal error and cumulative distribution of error are a fine way to present the data - a confusion matrix would tell us which actual sounds the decoder is confusing. Just looking at errors could result in some funky things where you reduce the error generally but never actually estimate the correct location.

We considered confusion matrices early on in our study but they were not easily interpretable or insightful, likely due to the relatively low discrimination ability of the mouse model with +/- 30º error after extensive training. Therefore, we reasoned that in passively listening mice (and likely trained mice too) with limited trial repetitions, an undersampled and diffuse confusion matrix is expected which is not an ideal means of visualizing and comparing decoding errors. Hence we relied on cumulative error distributions.

Do your top-ranked units have stronger projections onto your 10-40 principal components?

It would be interesting to know if the components are mostly taking into account those 30ish percent of the population that is dependent upon azimuth.

Inspection of PC loadings across units ranked based on response dependency to stimulus azimuth does not show a consistent stronger projection of top ranked units onto the first 10-40 principal components (Author response image 3).

Author response image 3.

PC loading matrices for each recorded mouse. The units recorded in each mouse are ranked in descending order of response dependency to stimulus azimuth based on  the p value of the chi square test. Units above the red dotted line display a chi square p value < 0.05, units below this line have p values >= 0.05.

How much overlap is there in the tuning of the top-ranked units?

This is quite varying from mouse to mouse and imaging vs electrophysiology, which makes it hard to make a generalization since this might depend on the unique DCIC population sampled in each mouse.

I'm not really sure I follow what the nS/N adds - it doesn't really measure tuning but it seems to be introduced to discuss/extract some measure of tuning.

nS/N is used to quantify how noisy neurons are, independent of how sensitive their responses are to the stimulus azimuth.

Is the noise correlation - observed to become more positive - for more contralateral stimuli a product of higher firing rates due to a more preferred stimulus presentation or a real effect in the data? Was there any relationship between distance and strength of observed noise correlation in the DCIC?

We observed a consistent and homogeneous trend of pairwise noise correlation distributions either shifted or tailed towards more positive values across stimulus azimuths, for imaging and electrophysiology datasets (Author response image 3). The lower firing frequency observed in neuropixels recordings in response to ipsilateral azimuths could have affected the statistical power of the comparison between the pairwise noise correlation coefficient distribution to its randomized chance level, but the overall histogram shapes qualitatively support this consistent trend across azimuths (Author response image 4).

Author response image 4.

Distribution histograms for the pairwise correlation coefficients (Kendall tau) from pairs of simultaneously recorded top ranked units across mice (blue) compared to the chance level distribution obtained through randomization of the temporal structure of each unit’s activity to break correlations (purple). Vertical lines show the medians of these distributions. Imaging data comes from n = 12 mice and neuropixels data comes from n = 4 mice.

Typos:

'a population code consisting on the simultaneous" > should on be of?

'half of the trails' > trails should be trials?

'referncing the demuxed channels' > should it be demixed?

Corrected.

eLife Assessment

The paper reports the important discovery that the mouse dorsal inferior colliculus, an auditory midbrain area, encodes sound location. The evidence supporting the claims is solid, being supported by both optical and electrophysiological recordings. The observations described should be of interest to auditory researchers studying the neural mechanisms of sound localization and the role of noise correlations in population coding.

Reviewer #1 (Public review):

Summary:

In this study, the authors address whether the dorsal nucleus of the inferior colliculus (DCIC) in mice encodes sound source location within the front horizontal plane (i.e., azimuth). They do this using volumetric two-photon Ca2+ imaging and high-density silicon probes (Neuropixels) to collect single-unit data. Such recordings are beneficial because they allow large populations of simultaneous neural data to be collected. Their main results and the claims about those results are the following:<br /> (1) DCIC single-unit responses have high trial-to-trial variability (i.e., neural noise);<br /> (2) approximately 32% to 40% of DCIC single units have responses that are sensitive to sound source azimuth;<br /> (3) single-trial population responses (i.e., the joint response across all sampled single units in an animal) encode sound source azimuth "effectively" (as stated in the title) in that localization decoding error matches average mouse discrimination thresholds;<br /> (4) DCIC can encode sound source azimuth in a similar format to that in the central nucleus of the inferior colliculus (as stated in the Abstract);<br /> (5) evidence of noise correlation between pairs of neurons exists;<br /> and 6) noise correlations between responses of neurons help reduce population decoding error.<br /> While simultaneous recordings are not necessary to demonstrate results #1, #2, and #4, they are necessary to demonstrate results #3, #5, and #6.

Strengths:<br /> - Important research question to all researchers interested in sensory coding in the nervous system.<br /> - State-of-the-art data collection: volumetric two-photon Ca2+ imaging and extracellular recording using high-density probes. Large neuronal data sets.<br /> - Confirmation of imaging results (lower temporal resolution) with more traditional microelectrode results (higher temporal resolution).<br /> - Clear and appropriate explanation of surgical and electrophysiological methods. I cannot comment on the appropriateness of the imaging methods.

Strength of evidence for the claims of the study:

(1) DCIC single-unit responses have high trial-to-trial variability -<br /> The authors' data clearly shows this.

(2) Approximately 32% to 40% of DCIC single units have responses that are sensitive to sound source azimuth -<br /> The sensitivity of each neuron's response to sound source azimuth was tested with a Kruskal-Wallis test, which is appropriate since response distributions were not normal. Using this statistical test, only 8% of neurons (median for imaging data) were found to be sensitive to azimuth, and the authors noted this was not significantly different than the false positive rate. The Kruskal-Wallis test was not reported for electrophysiological data. The authors suggested that low numbers of azimuth-sensitive units resulting from the statistical analysis may be due to the combination of high neural noise and relatively low number of trials, which would reduce statistical power of the test. This is likely true, and highlights a weakness in the experimental design (i.e., relatively small number of trials). The authors went on to perform a second test of azimuth sensitivity-a chi-squared test-and found 32% (imaging) and 40% (e-phys) of single units to have statistically significant sensitivity. However, the use of a chi-squared test is questionable because it is meant to be used between two categorical variables, and neural response had to be binned before applying the test.

(3) Single-trial population responses encode sound source azimuth "effectively" in that localization decoding error matches average mouse discrimination thresholds -<br /> If only one neuron in a population had responses that were sensitive to azimuth, we would expect that decoding azimuth from observation of that one neuron's response would perform better than chance. By observing the responses of more than one neuron (if more than one were sensitive to azimuth), we would expect performance to increase. The authors found that decoding from the whole population response was no better than chance. They argue (reasonably) that this is because of overfitting of the decoder model-too few trials were used to fit too many parameters-and provide evidence from decoding combined with principal components analysis which suggests that overfitting is occurring. What is troubling is the performance of the decoder when using only a handful of "top-ranked" neurons (in terms of azimuth sensitivity) (Fig. 4F and G). Decoder performance seems to increase when going from one to two neurons, then decreases when going from two to three neurons, and doesn't get much better for more neurons than for one neuron alone. It seems likely there is more information about azimuth in the population response, but decoder performance is not able to capture it because spike count distributions in the decoder model are not being accurately estimated due to too few stimulus trials (14, on average). In other words, it seems likely that decoder performance is underestimating the ability of the DCIC population to encode sound source azimuth.

To get a sense of how effective a neural population is at coding a particular stimulus parameter, it is useful to compare population decoder performance to psychophysical performance. Unfortunately, mouse behavioral localization data do not exist. Instead, the authors compare decoder error to mouse left-right discrimination thresholds published previously by a different lab. However, this comparison is inappropriate because the decoder and the mice were performing different perceptual tasks. The decoder is classifying sound sources to 1 of 13 locations from left to right, whereas the mice were discriminating between left or right sources centered around zero degrees. The errors in these two tasks represent different things. The two data sets may potentially be more accurately compared by extracting information from the confusion matrices of population decoder performance. For example, when the stimulus was at -30 deg, how often did the decoder classify the stimulus to a lefthand azimuth? Likewise, when the stimulus was +30 deg, how often did the decoder classify the stimulus to a righthand azimuth?

(4) DCIC can encode sound source azimuth in a similar format to that in the central nucleus of the inferior colliculus -<br /> It is unclear what exactly the authors mean by this statement in the Abstract. There are major differences in the encoding of azimuth between the two neighboring brain areas: a large majority of neurons in the CNIC are sensitive to azimuth (and strongly so), whereas the present study shows a minority of azimuth-sensitive neurons in the DCIC. Furthermore, CNIC neurons fire reliably to sound stimuli (low neural noise), whereas the present study shows that DCIC neurons fire more erratically (high neural noise).

(5) Evidence of noise correlation between pairs of neurons exists -<br /> The authors' data and analyses seem appropriate and sufficient to justify this claim.

(6) Noise correlations between responses of neurons help reduce population decoding error -<br /> The authors show convincing analysis that performance of their decoder increased when simultaneously measured responses were tested (which include noise correlation) than when scrambled-trial responses were tested (eliminating noise correlation). This makes it seem likely that noise correlation in the responses improved decoder performance. The authors mention that the naïve Bayesian classifier was used as their decoder for computational efficiency, presumably because it assumes no noise correlation and, therefore, assumes responses of individual neurons are independent of each other across trials to the same stimulus. The use of a decoder that assumes independence seems key here in testing the hypothesis that noise correlation contains information about sound source azimuth. The logic of using this decoder could be more clearly spelled out to the reader. For example, if the null hypothesis is that noise correlations do not carry azimuth information, then a decoder that assumes independence should perform the same whether population responses are simultaneous or scrambled. The authors' analysis showing a difference in performance between these two cases provides evidence against this null hypothesis.

Minor weakness:<br /> - Most studies of neural encoding of sound source azimuth are done in a noise-free environment, but the experimental setup in the present study had substantial background noise. This complicates comparison of the azimuth tuning results in this study to those of other studies. One is left wondering if azimuth sensitivity would have been greater in the absence of background noise, particularly for the imaging data where the signal was only about 12 dB above the noise.

Reviewer #2 (Public review):

In the present study, Boffi et al. investigate the manner in which the dorsal cortex of the of the inferior colliculus (DCIC), an auditory midbrain area, encodes sound location azimuth in awake, passively listening mice. By employing volumetric calcium imaging (scanned temporal focusing or s-TeFo), complemented with high-density electrode electrophysiological recordings (neuropixels probes), they show that sound-evoked responses are exquisitely noisy, with only a small portion of neurons (units) exhibiting spatial sensitivity. Nevertheless, a naïve Bayesian classifier was able to predict the presented azimuth based on the responses from small populations of these spatially sensitive units. A portion of the spatial information was provided by correlated trial-to-trial response variability between individual units (noise correlations). The study presents a novel characterization of spatial auditory coding in a non-canonical structure, representing a noteworthy contribution specifically to the auditory field and generally to systems neuroscience, due to its implementation of state-of-the-art techniques in an experimentally challenging brain region. However, nuances in the calcium imaging dataset and the naïve Bayesian classifier warrant caution when interpreting some of the results.

Strengths:

The primary strength of the study lies in its methodological achievements, which allowed the authors to collect a comprehensive and novel dataset. While the DCIC is a dorsal structure, it extends up to a millimetre in depth, making it optically challenging to access in its entirety. It is also more highly myelinated and vascularised compared to e.g., the cerebral cortex, compounding the problem. The authors successfully overcame these challenges and present an impressive volumetric calcium imaging dataset. Furthermore, they corroborated this dataset with electrophysiological recordings, which produced overlapping results. This methodological combination ameliorates the natural concerns that arise from inferring neuronal activity from calcium signals alone, which are in essence an indirect measurement thereof.

Another strength of the study is its interdisciplinary relevance. For the auditory field, it represents a significant contribution to the question of how auditory space is represented in the mammalian brain. "Space" per se is not mapped onto the basilar membrane of the cochlea and must be computed entirely within the brain. For azimuth, this requires the comparison between miniscule differences between the timing and intensity of sounds arriving at each ear. It is now generally thought that azimuth is initially encoded in two, opposing hemispheric channels, but the extent to which this initial arrangement is maintained throughout the auditory system remains an open question. The authors observe only a slight contralateral bias in their data, suggesting that sound source azimuth in the DCIC is encoded in a more nuanced manner compared to earlier processing stages of the auditory hindbrain. This is interesting because it is also known to be an auditory structure to receive more descending inputs from the cortex.

Systems neuroscience continues to strive for the perfection of imaging novel, less accessible brain regions. Volumetric calcium imaging is a promising emerging technique, allowing the simultaneous measurement of large populations of neurons in three dimensions. But this necessitates corroboration with other methods, such as electrophysiological recordings, which the authors achieve. The dataset moreover highlights the distinctive characteristics of neuronal auditory representations in the brain. Its signals can be exceptionally sparse and noisy, which provide an additional layer of complexity in the processing and analysis of such datasets. This will undoubtedly be useful for future studies of other less accessible structures with sparse responsiveness.

Weaknesses:

Although the primary finding that small populations of neurons carry enough spatial information for a naïve Bayesian classifier to reasonably decode the presented stimulus is not called into question, certain idiosyncrasies, in particular the calcium imaging dataset and model, complicate specific interpretations of the model output, and the readership is urged to interpret these aspects of the study's conclusions with caution.

I remain in favour of volumetric calcium imaging as a suitable technique for the study, but the presently constrained spatial resolution is insufficient to unequivocally identify regions of interest as cell bodies (and are instead referred to as "units" akin to those of electrophysiological recordings). It remains possible that the imaging set is inadvertently influenced by non-somatic structures (including neuropil), which could report neuronal activity differently than cell bodies. Due to the lack of a comprehensive ground-truth comparison in this regard (which to my knowledge is impossible to achieve with current technology), it is difficult to imagine how many informative such units might have been missed because their signals were influenced by spurious, non-somatic signals, which could have subsequently misled the models. The authors reference the original Nature Methods article (Prevedel et al., 2016) throughout the manuscript, presumably in order to avoid having to repeat previously published experimental metrics. But the DCIC is neither the cortex nor hippocampus (for which the method was originally developed) and may not have the same light scattering properties (not to mention neuronal noise levels). Although the corroborative electrophysiology data largely eleviates these concerns for this particular study, the readership should be cognisant of such caveats, in particular those who are interested in implementing the technique for their own research.

A related technical limitation of the calcium imaging dataset is the relatively low number of trials (14) given the inherently high level of noise (both neuronal and imaging). Volumetric calcium imaging, while offering a uniquely expansive field of view, requires relatively high average excitation laser power (in this case nearly 200 mW), a level of exposure the authors may have wanted to minimise by maintaining a low number of repetitions, but I yield to them to explain. Calcium imaging is also inherently slow, requiring relatively long inter-stimulus intervals (in this case 5 s). This unfortunately renders any model designed to predict a stimulus (in this case sound azimuth) from particularly noisy population neuronal data like these as highly prone to overfitting, to which the authors correctly admit after a model trained on the entire raw dataset failed to perform significantly above chance level. This prompted them to feed the model only with data from neurons with the highest spatial sensitivity. This ultimately produced reasonable performance (and was implemented throughout the rest of the study), but it remains possible that if the model was fed with more repetitions of imaging data, its performance would have been more stable across the number of units used to train it. (All models trained with imaging data eventually failed to converge.) However, I also see these limitations as an opportunity to improve the technology further, which I reiterate will be generally important for volume imaging of other sparse or noisy calcium signals in the brain.

Indeed, in separate comments to these remarks, the authors confirmed that the low number of trials was technically limited, to which I emphasise is to no fault of their own. However, they also do not report this as a typical imaging constraint, such as photobleaching, but rather because the animals exhibited signs of stress and discomfort at longer imaging periods. From an animal welfare perspective, I would encourage the authors to state this in the methods for transparency. It would demonstrate their adherence to animal welfare policies, which I find to be an incredibly strong argument for limiting the number of trials in their study.

Transitioning to the naïve Bayesian classifier itself, I first openly ask the authors to justify their choice of this specific model. There are countless types of classifiers for these data, each with their own pros and cons. Did they actually try other models (such as support vector machines), which ultimately failed? If so, these negative results (even if mentioned en passant) would be extremely valuable to the community, in my view. I ask this specifically because different methods assume correspondingly different statistical properties of the input data, and to my knowledge naïve Bayesian classifiers assume that predictors (neuronal responses) are assumed to be independent within a class (azimuth). As the authors show that noise correlations are informative in predicting azimuth, I wonder why they chose a model that doesn't take advantage of these statistical regularities. It could be because of technical considerations (they mention computing efficiency), but I am left generally uncertain about the specific logic that was used to guide the authors through their analytical journey.

In a revised version of the manuscript, the authors indeed justify their choice of the naïve Bayesian classifier as a conservative approach (not taking into account noise correlations), which could only improve with other models (that do). They even tested various other commonly used models, such as support vector machines and k-nearest neighbours, to name a few, but do not report these efforts in the main manuscript. Interestingly, these models, which I supposed would perform better in fact did not overall - a finding that I have no way of interpreting but nevertheless find interesting. I would thus encourage the authors to include these results in a figure supplement and mention it en passant while justifying their selection of model (but please include detailed model parameters in the methods section).

That aside, there remain other peculiarities in model performance that warrant further investigation. For example, what spurious features (or lack of informative features) in these additional units prevented the models of imaging data from converging? In an orthogonal question, did the most spatially sensitive units share any detectable tuning features? A different model trained with electrophysiology data in contrast did not collapse in the range of top-ranked units plotted. Did this model collapse at some point after adding enough units, and how well did that correlate with the model for the imaging data? How well did the form (and diversity) of the spatial tuning functions as recorded with electrophysiology resemble their calcium imaging counterparts? These fundamental questions could be addressed with more basic, but transparent analyses of the data (e.g., the diversity of spatial tuning functions of their recorded units across the population). Even if the model extracts features that are not obvious to the human eye in traditional visualisations, I would still find this interesting.

Although these questions were not specifically addressed in the revised version of the manuscript, I also admit that I did not indent do assert that these should necessarily fall within the scope of the present study. I rather posed them as hypothetical directions one could pursue in future studies. Finally, further concerns I had with statements regarding the physiological meaning of the findings have been ameliorated by nicely modified statements, thus bringing transparency to the readership, which I appreciate.

In summary, the present study represents a significant body of work that contributes substantially to the field of spatial auditory coding and systems neuroscience. However, limitations of the imaging dataset and model as applied in the study muddles concrete conclusions about how the DCIC precisely encodes sound source azimuth and even more so to sound localisation in a behaving animal. Nevertheless, it presents a novel and unique dataset, which, regardless of secondary interpretation, corroborates the general notion that auditory space is encoded in an extraordinarily complex manner in the mammalian brain.

Reviewer #3 (Public review):

Summary:

Boffi and colleagues sought to quantify the single-trial, azimuthal information in the dorsal cortex of the inferior colliculus (DCIC), a relatively understudied subnucleus of the auditory midbrain. They accomplished this by using two complementary recording methods while mice passively listened to sounds at different locations: calcium imaging that recorded large neuronal populations but with poor temporal precision and multi-contact electrode arrays that recorded smaller neuronal populations with exact temporal precision. DCIC neurons respond variably, with inconsistent activity to sound onset and complex azimuthal tuning. Some of this variably was explained by ongoing head movements. The authors used a naïve Bayes decoder to probe the azimuthal information contained in the response of DCIC neurons on single trials. The decoder failed to classify sound location better than chance when using the raw population responses but performed significantly better than chance when using the top principal components of the population. Units with the most azimuthal tuning were distributed throughout the DCIC, possessed contralateral bias, and positively correlated responses. Interestingly, inter-trial shuffling decreased decoding performance, indicating that noise correlations contributed to decoder performance. Overall, Boffi and colleagues, quantified the azimuthal information available in the DCIC while mice passively listened to sounds, a first step in evaluating if and how the DCIC could contribute to sound localization.

Strengths:

The authors should be commended for collection of this dataset. When done in isolation (which is typical), calcium imaging and linear array recordings have intrinsic weaknesses. However, those weaknesses are alleviated when done in conjunction - especially when the data is consistent. This data set is extremely rich and will be of use for those interested in auditory midbrain responses to variable sound locations, correlations with head movements, and neural coding.

The DCIC neural responses are complex with variable responses to sound onset, complex azimuthal tuning and large inter-sound interval responses. Nonetheless, the authors do a decent job in wrangling these complex responses: finding non-canonical ways of determining dependence on azimuth and using interpretable decoders to extract information from the population.

Weaknesses:

The decoding results are a bit strange, likely because the population response is quite noisy on any given trial. Raw population responses failed to provide sufficient information concerning azimuth for significant decoding. Importantly, the decoder performed better than chance when certain principal components or top ranked units contributed but did not saturate with the addition of components or top ranked units. So, although there is azimuthal information in the recorded DCIC populations - azimuthal information appears somewhat difficult to extract.

Although necessary given the challenges associated with sampling many conditions with technically difficult recording methods, the limited number of stimulus repeats precludes interpretable characterization of the heterogeneity across the population. Nevertheless, the dataset is public so those interested can explore the diversity of the responses.

The observations from Boffi and colleagues raises the question: what drives neurons in the DCIC to respond? Sound azimuth appears to be a small aspect of the DCIC response. For example, the first 20 principal components which explain roughly 80% of the response variance are insufficient input for the decoder to predict sound azimuth above chance. Furthermore, snout and ear movements correlate with the population response in the DCIC (the ear movements are particularly peculiar given they seem to predict sound presentation). Other movements may be of particular interest to control for (e.g. eye movements are known to interact with IC responses in the primate). These observations, along with reported variance to sound onsets and inter-sound intervals, question the impact of azimuthal information emerging from DCIC responses. This is certainly out of scope for any one singular study to answer, but, hopefully, future work will elucidate the dominant signals in the DCIC population. It may be intuitive that engagement in a sound localization task may push azimuthal signals to the forefront of DCIC response, but azimuthal information could also easily be overtaken by other signals (e.g. movement, learning).

Boffi and colleagues set out to parse the azimuthal information available in the DCIC on a single trial. They largely accomplish this goal and are able to extract this information when allowing the units that contain more information about sound location to contribute to their decoding (e.g., through PCA or decoding on their activity specifically). Interestingly, they also found that positive noise correlations between units with similar azimuthal preferences facilitate this decoding - which is unusual given that this is typically thought to limit information. The dataset will be of value to those interested in the DCIC and to anyone interested in the role of noise correlations in population coding. Although this work is first step into parsing the information available in the DCIC, it remains difficult to interpret if/how this azimuthal information is used in localization behaviors of engaged mice.

Author response:

Reviewer #1 (Public Review):

Padilha et al. aimed to find prospective metabolite biomarkers in serum of children aged 6-59 months that were indicative of neurodevelopmental outcomes. The authors leveraged data and samples from the cross-sectional Brazilian National Survey on Child Nutrition (ENANI-2019), and an untargeted multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) approach was used to measure metabolites in serum samples (n=5004) which were identified via a large library of standards. After correlating the metabolite levels against the developmental quotient (DQ), or the degree of which age-appropriate developmental milestones were achieved as evaluated by the Survey of Well-being of Young Children, serum concentrations of phenylacetylglutamine (PAG), cresol sulfate (CS), hippuric acid (HA) and trimethylamine-N-oxide (TMAO) were significantly negatively associated with DQ. Examination of the covariates revealed that the negative associations of PAG, HA, TMAO and valine (Val) with DQ were specific to younger children (-1 SD or 19 months old), whereas creatinine (Crtn) and methylhistidine (MeHis) had significant associations with DQ that changed direction with age (negative at -1 SD or 19 months old, and positive at +1 SD or 49 months old). Further, mediation analysis demonstrated that PAG was a significant mediator for the relationship of delivery mode, child's diet quality and child fiber intake with DQ. HA and TMAO were additional significant mediators of the relationship of child fiber intake with DQ.

Strengths of this study include the large cohort size and study design allowing for sampling at multiple time points along with neurodevelopmental assessment and a relatively detailed collection of potential confounding factors including diet. The untargeted metabolomics approach was also robust and comprehensive allowing for level 1 identification of a wide breadth of potential biomarkers. Given their methodology, the authors should be able to achieve their aim of identifying candidate serum biomarkers of neurodevelopment for early childhood. The results of this work would be of broad interest to researchers who are interested in understanding the biological underpinnings of development and also for tracking development in pediatric populations, as it provides insight for putative mechanisms and targets from a relevant human cohort that can be probed in future studies. Such putative mechanisms and targets are currently lacking in the field due to challenges in conducting these kind of studies, so this work is important.

However, in the manuscript's current state, the presentation and analysis of data impede the reader from fully understanding and interpreting the study's findings.

Particularly, the handling of confounding variables is incomplete. There is a different set of confounders listed in Table 1 versus Supplementary Table 1 versus Methods section Covariates versus Figure 4. For example, Region is listed in Supplementary Table 1 but not in Table 1, and Mode of Delivery is listed in Table 1 but not in Supplementary Table 1. Many factors are listed in Figure 4 that aren't mentioned anywhere else in the paper, such as gestational age at birth or maternal pre-pregnancy obesity.

We thank the reviewer for their comment. We would like to clarify that initially, the tables had different variables because they have different purposes. Table 1 aims to characterize the sample on variables directly related to the children’s and mother’s features and their nutritional status. Supplementary File 1(previously named supplementary table 1) summarizes the sociodemographic distribution of the development quotient. Neither of the tables concerned the metabolite-DQ relationships and their potential covariates, they only provide context for subsequent analyses by characterizing the sample and the outcome. Instead, the covariates included in the regression models were selected using the Direct Acyclic Graph presented in Figure 1.

To avoid this potential confusion however, we included the same variables in Table 1 and Supplementary File 1(page 38) and we discussed the selection of model covariates in Figure 4 in more detail here in the letter and in the manuscript.

The authors utilize the directed acrylic graph (DAG) in Figure 4 to justify the further investigation of certain covariates over others. However, the lack of inclusion of the microbiome in the DAG, especially considering that most of the study findings were microbial-derived metabolite biomarkers, appears to be a fundamental flaw. Sanitation and micronutrients are proposed by the authors to have no effect on the host metabolome, yet sanitation and micronutrients have both been demonstrated in the literature to affect microbiome composition which can in turn affect the host metabolome.

Thank you for your comment. We appreciate that the use of DAG and lack of the microbiome in the DAG are concerns. This has been already discussed in reply #1 to the editor that has been pasted below for convenience:

Thank you for the comment and suggestions. It is important to highlight that there is no data on microbiome composition. We apologize if there was an impression such data is available. The main goal of conducting this national survey was to provide qualified and updated evidence on child nutrition to revise and propose new policies and nutritional guidelines for this demographic. Therefore, collection of stool derived microbiome (metagenomic) data was not one of the objectives of ENANI-2019. This is more explicitly stated as a study limitation in the revised manuscript on page 17, lines 463-467:

“Lastly, stool microbiome data was not collected from children in ENANI-2019 as it was not a study objective in this large population-based nutritional survey. However, the lack of microbiome data does not reduce the importance/relevance, since there is no evidence that microbiome and factors affecting microbiome composition are confounders in the association between serum metabolome and child development.”

Besides, one must consider the difficulties and costs in collecting and analyzing microbiome composition in a large population-based survey. In contrast, the metabolome data has been considered a priority as there was already blood specimens collected to inform policy on micronutrient deficiencies in Brazil. However, due to funding limitations we had to perform the analysis in a subset of our sample, still representative and large enough to test our hypothesis with adequate study power (more details below).

We would like to argue that there is no evidence that microbiome and factors affecting microbiome composition are confounders on the association between serum metabolome and child development. First, one should revisit the properties of a confounder according to the epidemiology literature that in short states that confounding refers to an alternative explanation for a given conclusion, thus constituting one of the main problems for causal inference (Kleinbaum, Kupper, and Morgenstern, 1991; Greenland & Robins, 1986; VanderWeele, 2019). In our study, we highlight that certain serum metabolites associated with the developmental quotient (DQ) in children were circulating metabolites (e.g., cresol sulfate, hippuric acid, phenylacetylglutamine, TMAO) previously reported to depend on dietary exposures, host metabolism and gut microbiota activity. Our discussion cites other published work, including animal models and observational studies, which have reported how these bioactive metabolites in circulation are co-metabolized by commensal gut microbiota, and may play a role in neurodevelopment and cognition as mediated by environmental exposures early in life.

In fact, the literature on the association between microbiome and infant development is very limited. We performed a search using terms ‘microbiome’ OR ‘microbiota’ AND ‘child development’ AND ‘systematic’ OR ‘meta-analysis’ and found only one study: ‘Associations between the human immune system and gut microbiome with neurodevelopment in the first 5 years of life: A systematic scoping review’ (DOI 10.1002/dev.22360). The authors conclude: ‘while the immune system and gut microbiome are thought to have interactive impacts on the developing brain, there remains a paucity of published studies that report biomarkers from both systems and associations with child development outcomes.’ It is important to highlight that our criteria to include confounders on the directed acyclic graph (DAG) was based on the literature of systematic reviews or meta-analysis and not on single isolated studies.

In summary, we would like to highlight that there is no microbiome data in ENANI-2019 and in the event such data was present, we are confident that based on the current stage of the literature, there is no evidence to consider such construct in the DAG, as this procedure recommends that only variables associated with the exposure and the outcome should be included. Please find more details on DAG below.

Moreover, we would like to clarify that we have not stated that sanitation and micronutrients have no effect on the serum metabolome, instead, these constructs were not considered on the DAG.

To make it clearer, we have modified the passage about DAG in the methods section. New text, page 9, lines 234-241:

“The subsequent step was to disentangle the selected metabolites from confounding variables. A Directed Acyclic Graph (DAG; Breitling et al., 2021) was used to more objectively determine the minimally sufficient adjustments for the regression models to account for potentially confounding variables while avoiding collider variables and variables in the metabolite-DQ causal pathways, which if controlled for would unnecessarily remove explained variance from the metabolites and hamper our ability to detect biomarkers. To minimize bias from subjective judgments of which variables should and should not be included as covariates, the DAG only included variables for which there was evidence from systematic reviews or meta-analysis of relationships with both the serum metabolome and DQ (Figure 1). Birth weight, breastfeeding, child's diet quality, the child's nutritional status, and the child's age were the minimal adjustments suggested by the DAG. Birth weight was a variable with high missing data, and indicators of breastfeeding practice data (referring to exclusive breastfeeding until 6 months and/or complemented until 2 years) were collected only for children aged 0–23 months. Therefore, those confounders were not included as adjustments. Child's diet quality was evaluated as MDD, the child's nutritional status as w/h z-score, and the child's age in months.”

Additionally, the authors emphasized as part of the study selection criteria the following, "Due to the costs involved in the metabolome analysis, it was necessary to further reduce the sample size. Then, samples were stratified by age groups (6 to 11, 12 to 23, and 24 to 59 months) and health conditions related to iron metabolism, such as anemia and nutrient deficiencies. The selection process aimed to represent diverse health statuses, including those with no conditions, with specific deficiencies, or with combinations of conditions. Ultimately, through a randomized process that ensured a balanced representation across these groups, a total of 5,004 children were selected for the final sample (Figure 1)."

Therefore, anemia and nutrient deficiencies are assumed by the reader to be important covariates, yet, the data on the final distribution of these covariates in the study cohort is not presented, nor are these covariates examined further.

Thank you for the comments. We apologize for the misunderstanding and will amend the text to make our rationale clearer in the revised version of the manuscript.

We believed the original text was clear enough in stating that the sampling process was performed aiming to maintain the representativeness of the original sample. This sampling process considered anemia and nutritional deficiencies, among other variables. However, we did not aim to include all relevant covariates of the DQ-metabolome relationship; these were decided using the DAG, as described in the manuscript and other sessions of this letter. Therefore, we would like to emphasize that our description of the sampling process does not assumes anemia and nutritional deficiencies are important covariates for the DQ-metabolome relationship.

We rewrote this text part, page 11, lines 279-285:

“Due to the costs involved in the metabolome analysis, it was necessary to reduce the sample size that is equivalent to 57% of total participants from ENANI-2019 with stored blood specimens. Therefore, the infants were stratified by age groups (6 to 11, 12 to 23, and 24 to 59 months) and health conditions such as anemia and micronutrient deficiencies. The selection process aimed to represent diverse health statuses to the original sample. Ultimately, 5,004 children were selected for the final sample through a random sampling process that ensured a balanced representation across these groups (Figure 2).”

The inclusion of specific covariates in Table 1, Supplementary Table 1, the statistical models, and the mediation analysis is thus currently biased as it is not well justified.

We appreciate the reviewer comment. However, it would have been ideal to receive a comment/critic with a clearer and more straightforward argumentation, so we could try to address it based on our interpretation.

Please refer to our response to item #1 above regarding the variables in the tables and figures. The covariates in the statistical models were selected using the DAG, which is a cutting-edge procedure that aims to avoid bias and overfitting, a common situation when confounders are adjusted for without a clear rationale. We elaborate on the advantages of using the DAG in response to item #6 and in page 9 of the manuscript. The statistical models we use follow the best practices in the field when dealing with a large number of collinear predictors and a continuous outcome (see our response to the editor’s 4th comment). Finally, the mediation analyses were done to explore a few potential explanations for our results from the PLSR and multiple regression analyses. We only ran mediation analyses for plausible mechanisms for which the variables of interest were available in our data. Please see our response to reviewer 3’s item #1 for a more detailed explanation on the mediation analysis.

Finally, it is unclear what the partial-least squares regression adds to the paper, other than to discard potentially interesting metabolites found by the initial correlation analysis.

Thank you for the question. As explained in response to the editor’s item #4, PLS-based analyses are among the most commonly used analyses for parsing metabolomic data (Blekherman et al., 2011; Wold et al., 2001; Gromski et al. 2015). This procedure is especially appropriate for cases in which there are multiple collinear predictor variables as it allows us to compare the predictive value of all the variables without relying on corrections for multiple testing. Testing each metabolite in separate correlations corrected for multiple comparisons is less appropriate because the correlated nature of the metabolites means the comparisons are not truly independent and would cause the corrections (which usually assume independence) to be overly strict. As such, we only rely on the correlations as an initial, general assessment that gives context to subsequent, more specific analyses. Given that our goal is to select the most predictive metabolites, discarding the less predictive metabolites is precisely what we aim to achieve. As explained above and in response to the editor’s item #4, the PLSR allows us to reach that goal without introducing bias in our estimates or losing statistical power.  

Reviewer #2 (Public Review):

A strength of the work lies in the number of children Padilha et al. were able to assess (5,004 children aged 6-59 months) and in the extensive screening that the Authors performed for each participant. This type of large-scale study is uncommon in low-to-middle-income countries such as Brazil.

The Authors employ several approaches to narrow down the number of potentially causally associated metabolites.

Could the Authors justify on what basis the minimum dietary diversity score was dichotomized? Were sensitivity analyses undertaken to assess the effect of this dichotomization on associations reported by the article? Consumption of each food group may have a differential effect that is obscured by this dichotomization.

Thank you for the observation. We would like to emphasize that the child's diet quality was assessed using the minimum dietary diversity (MDD) indicator proposed by the WHO (World Health Organization & United Nations Children’s Fund (UNICEF), 2021). This guideline proposes the cutoff used in the present study. We understand the reviewer’s suggestion to use the consumption of healthy food groups as an evaluation of diet quality, but we chose to follow the WHO proposal to assess dietary diversity. This indicator is widely accepted and used as a marker and provides comparability and consistency with other published studies.

Could the Authors specify the statistical power associated with each analysis?

To the best of our knowledge, we are not aware of power calculation procedures for PLS-based analyses. However, given our large sample size, we do not believe power was an issue with the analyses. For our regression analyses, which typically have 4 predictors, we had 95% power to detect an f-squared of 0.003 and an r of 0.05 in a two-sided correlation test considering an alpha level of 0.05.

New text, page 11, lines 296-298:

“Given the size of our sample, statistical power is not an issue in our analyses. Considering an alpha of 0.05 for a two-sided test, a sample size of 5000 has 95% power to detect a correlation of r = 0.05 and an effect of f2 = 0.003 in a multiple regression model with 4 predictors.”

Could the Authors describe in detail which metric they used to measure how predictive PLSR models are, and how they determined what the "optimal" number of components were?

We chose the model with the fewest number of components that maximized R2 and minimized root mean squared error of prediction (RMSEP). In the training data, the model with 4 components had a lower R2 but a lower RMSEP, therefore we chose the model with 3 components which had a higher R2 than the 4-component model and lower RMSEP than the model with 2 components. However, the number of components in the model did not meaningfully change the rank order of the metabolites on the VIP index.

New text, page 8, lines 220-224:

“To better assess the predictiveness of each metabolite in a single model, a PLSR was conducted. PLS-based analyses are the most commonly used analyses when determining the predictiveness of a large number of variables as they avoid issues with collinearity, sample size, and corrections for multiple-testing (Blekherman et al., 2011; Wold et al., 2001; Gromski et al. 2015).”

New text, page 12, lines 312-314:

“In PLSR analysis, the training data suggested that three components best predicted the data (the model with three components had the highest R2, and the root mean square error of prediction (RMSEP) was only slightly lower with four components). In comparison, the test data showed a slightly more predictive model with four components (Figure 3—figure supplement 2).”

The Authors use directed acyclic graphs (DAG) to identify confounding variables of the association between metabolites and DQ. Could the dataset generated by the Authors have been used instead? Not all confounding variables identified in the literature may be relevant to the dataset generated by the Authors.

Thank you for the question. The response is most likely no, the current dataset should not be used to define confounders as these must be identified based on the literature. The use of DAGs has been widely explored as a valid tool for justifying the choice of confounding factors in regression models in epidemiology. This is because DAGs allow for a clear visualization of causal relationships, clarify the complex relationships between exposure and outcome. Besides, DAGs demonstrate the authors' transparency by acknowledging factors reported as important but not included/collected in the study. This has been already discussed in reply #1 to the editor that has been pasted below for convenience.

Thank you for the comment and suggestions. It is important to highlight that there is no data on microbiome composition. We apologize if there was an impression such data is available. The main goal of conducting this national survey was to provide qualified and updated evidence on child nutrition to revise and propose new policies and nutritional guidelines for this demographic. Therefore, collection of stool derived microbiome (metagenomic) data was not one of the objectives of ENANI-2019. This is more explicitly stated as a study limitation in the revised manuscript on page 17, lines 463-467:

“Lastly, stool microbiome data was not collected from children in ENANI-2019 as it was not a study objective in this large population-based nutritional survey. However, the lack of microbiome data does not reduce the importance/relevance, since there is no evidence that microbiome and factors affecting microbiome composition are confounders in the association between serum metabolome and child development.”

Besides, one must consider the difficulties and costs in collecting and analyzing microbiome composition in a large population-based survey. In contrast, the metabolome data has been considered a priority as there was already blood specimens collected to inform policy on micronutrient deficiencies in Brazil. However, due to funding limitations we had to perform the analysis in a subset of our sample, still representative and large enough to test our hypothesis with adequate study power (more details below).

We would like to argue that there is no evidence that microbiome and factors affecting microbiome composition are confounders on the association between serum metabolome and child development. First, one should revisit the properties of a confounder according to the epidemiology literature that in short states that confounding refers to an alternative explanation for a given conclusion, thus constituting one of the main problems for causal inference (Kleinbaum, Kupper, and Morgenstern, 1991; Greenland & Robins, 1986; VanderWeele, 2019). In our study, we highlight that certain serum metabolites associated with the developmental quotient (DQ) in children were circulating metabolites (e.g., cresol sulfate, hippuric acid, phenylacetylglutamine, TMAO) previously reported to depend on dietary exposures, host metabolism and gut microbiota activity. Our discussion cites other published work, including animal models and observational studies, which have reported how these bioactive metabolites in circulation are co-metabolized by commensal gut microbiota, and may play a role in neurodevelopment and cognition as mediated by environmental exposures early in life.

In fact, the literature on the association between microbiome and infant development is very limited. We performed a search using terms ‘microbiome’ OR ‘microbiota’ AND ‘child development’ AND ‘systematic’ OR ‘meta-analysis’ and found only one study: ‘Associations between the human immune system and gut microbiome with neurodevelopment in the first 5 years of life: A systematic scoping review’ (DOI 10.1002/dev.22360). The authors conclude: ‘while the immune system and gut microbiome are thought to have interactive impacts on the developing brain, there remains a paucity of published studies that report biomarkers from both systems and associations with child development outcomes.’ It is important to highlight that our criteria to include confounders on the directed acyclic graph (DAG) was based on the literature of systematic reviews or meta-analysis and not on single isolated studies.

In summary, we would like to highlight that there is no microbiome data in ENANI-2019 and in the event such data was present, we are confident that based on the current stage of the literature, there is no evidence to consider such construct in the DAG, as this procedure recommends that only variables associated with the exposure and the outcome should be included. Please find more details on DAG below.

Moreover, we would like to clarify that we have not stated that sanitation and micronutrients have no effect on the serum metabolome, instead, these constructs were not considered on the DAG.

To make it clearer, we have modified the passage about DAG in the methods section. New text, page 9, lines 234-241:

“The subsequent step was to disentangle the selected metabolites from confounding variables. A Directed Acyclic Graph (DAG; Breitling et al., 2021) was used to more objectively determine the minimally sufficient adjustments for the regression models to account for potentially confounding variables while avoiding collider variables and variables in the metabolite-DQ causal pathways, which if controlled for would unnecessarily remove explained variance from the metabolites and hamper our ability to detect biomarkers. To minimize bias from subjective judgments of which variables should and should not be included as covariates, the DAG only included variables for which there was evidence from systematic reviews or meta-analysis of relationships with both the serum metabolome and DQ (Figure 1). Birth weight, breastfeeding, child's diet quality, the child's nutritional status, and the child's age were the minimal adjustments suggested by the DAG. Birth weight was a variable with high missing data, and indicators of breastfeeding practice data (referring to exclusive breastfeeding until 6 months and/or complemented until 2 years) were collected only for children aged 0–23 months. Therefore, those confounders were not included as adjustments. Child's diet quality was evaluated as MDD, the child's nutritional status as w/h z-score, and the child's age in months.”

Were the systematic reviews or meta-analyses used in the DAG performed by the Authors, or were they based on previous studies? If so, more information about the methodology employed and the studies included should be provided by the Authors.

Thank you for the question. The reviews or meta-analyses used in the DAG have been conducted by other authors in the field. This has been laid out more clearly in our methods section.

New text, page 9, lines 234-241:

“The subsequent step was to disentangle the selected metabolites from confounding variables. A Directed Acyclic Graph (DAG; Breitling et al., 2021) was used to more objectively determine the minimally sufficient adjustments for the regression models to account for potentially confounding variables while avoiding collider variables and variables in the metabolite-DQ causal pathways, which if controlled for would unnecessarily remove explained variance from the metabolites and hamper our ability to detect biomarkers. To minimize bias from subjective judgments of which variables should and should not be included as covariates, the DAG only included variables for which there was evidence from systematic reviews or meta-analysis of relationships with both the metabolome and DQ (Figure 1). Birth weight, breastfeeding, child's diet quality, the child's nutritional status, and the child's age were the minimal adjustments suggested by the DAG. Birth weight was a variable with high missing data, and indicators of breastfeeding practice data (referring to exclusive breastfeeding until 6 months and/or complemented until 2 years) were collected only for children aged 0–23 months. Therefore, those confounders were not included as adjustments. Child's diet quality was evaluated as MDD, the child's nutritional status as w/h z-score, and the child's age in months.”

Approximately 72% of children included in the analyses lived in households with a monthly income superior to the Brazilian minimum wage. The cohort is also biased towards households with a higher level of education. Both of these measures correlate with developmental quotient. Could the Authors discuss how this may have affected their results and how generalizable they are?

Thank you for your comment. This has been already discussed in reply #6 to the editor and that has been pasted below for convenience.

Thank you for highlighting this point. The ENANI-2019 is a population-based household survey with national coverage and representativeness for macroregions, sex, and one-year age groups (< 1; 1-1.99; 2-2.99; 3-3.99; 4-5). Furthermore, income quartiles of the census sector were used in the sampling. The study included 12,524 households 14,588 children, and 8,829 infants with blood drawn.

Due to the costs involved in metabolome analysis, it was necessary to further reduce the sample size to around 5,000 children that is equivalent to 57% of total participants from ENANI-2019 with stored blood specimens. To avoid a biased sample and keep the representativeness and generability, the 5,004 selected children were drawn from the total samples of 8,829 to keep the original distribution according age groups (6 to 11 months, 12 to 23 months, and 24 to 59 months), and some health conditions related to iron metabolism, e.g., anemia and nutrient deficiencies. Then, they were randomly selected to constitute the final sample that aimed to represent the total number of children with blood drawn. Hence, our efforts were to preserve the original characteristics of the sample and the representativeness of the original sample.

The ENANI-2019 study does not appear to present a bias towards higher socioeconomic status. Evidence from two major Brazilian population-based household surveys supports this claim. The 2017-18 Household Budget Survey (POF) reported an average monthly household income of 5,426.70 reais, while the Continuous National Household Sample Survey (PNAD) reported that in 2019, the nominal monthly per capita household income was 1,438.67 reais. In comparison, ENANI-2019 recorded a household income of 2,144.16 reais and a per capita income of 609.07 reais in infants with blood drawn, and 2,099.14 reais and 594.74 reais, respectively, in the serum metabolome analysis sample.

In terms of maternal education, the 2019 PNAD-Education survey indicated that 48.8% of individuals aged 25 or older had at least 11 years of schooling. When analyzing ENANI-2019 under the same metric, we found that 56.26% of ≥25 years-old mothers of infants with blood drawn had 11 years of education or more, and 51.66% in the metabolome analysis sample. Although these figures are slightly higher, they remain within a reasonable range for population studies.

It is well known that higher income and maternal education levels can influence child health outcomes, and acknowledging this, ENANI-2019 employed rigorous sampling methods to minimize selection biases. This included stratified and complex sampling designs to ensure that underrepresented groups were adequately included, reducing the risk of skewed conclusions. Therefore, the evidence strongly suggests that the ENANI-2019 sample is broadly representative of the Brazilian population in terms of both socioeconomic status and educational attainment.

Further to this, could the Authors describe how inequalities in access to care in the Brazilian population may have affected their results? Could they have included a measure of this possible discrepancy in their analyses?

Thank you for the concern.

The truth is that we are not in a position to answer this question because our study focused on gathering data on infant nutritional status and there is very limited information on access to care to allow us to hypothesize. Another important piece of information is that this national survey used sampling procedures that aimed to make the sample representative of the 15 million Brazilian infants under 5 years. Therefore, the sample is balanced according to socio-economic strata, so there is no evidence to make us believe inequalities in access to health care would have played a role.

The Authors state that the results of their study may be used to track children at risk for developmental delays. Could they discuss the potential for influencing policies and guidelines to address delayed development due to malnutrition and/or limited access to certain essential foods?

The point raised by the reviewer is very relevant. Recognizing that dietary and microbial derived metabolites involved in the gut-brain axis could be related to children's risk of developmental delays is the first step to bringing this topic to the public policy agenda. We believe the results can contribute to the literature, which should be used to accumulate evidence to overcome knowledge gaps and support the formulation and redirection of public policies aimed at full child growth and development; the promotion of adequate and healthy nutrition and food security; the encouragement, support, and protection of breastfeeding; and the prevention and control of micronutrient deficiencies.  

Reviewer #3 (Public Review):

The ENANI-2019 study provides valuable insights into child nutrition, development, and metabolomics in Brazil, highlighting both challenges and opportunities for improving child health outcomes through targeted interventions and further research.

Readers might consider the following questions:

(1) Should investigators study the families through direct observation of diet and other factors to look for a connection between food taken in and gut microbiome and child development?

As mentioned before, the ENANI-2019 did not collect data on stool derived microbiome. However, there is data on child dietary intake with 24-hour recall that can be further explored in other studies.

(2) Can an examination of the mother's gut microbiome influence the child's microbiome? Can the mother or caregiver's microbiome influence early childhood development?

The questions raised by the reviewer are interesting and has been explored by other authors. However, we do not have microbiota data from the child nor from the mother/caregiver.

(3) Is developmental quotient enough to study early childhood development? Is it comprehensive enough?

Yes, we are confident it is comprehensive enough.

According to the World Health Organization, the term Early Childhood Development (ECD) refers to the cognitive, physical, language, motor, social and emotional development between 0 - 8 years of age. The SWCY milestones assess the domains of cognition, language/communication and motor. Therefore, it has enough content validity to represent ECD.

The SWYC is recommended for screening ECD by the American Society of Pediatrics. Furthermore, we assessed the internal consistency of the SWYC milestones questionnaire using ENANI-2019 data and Cronbach's alpha. The findings indicated satisfactory reliability (0.965; 95% CI: 0.963–0.968).

The SWCY is a screening instrument and indicates if the ECD is not within the expected range. If one of the above-mentioned domains are not achieved as expected the child may be at risk of ECD delay. Therefore, DQ<1 indicates that a child has not reached the expected ECD for the age group. We cannot say that children with DQ≥1 have full ECD, since we do not assess the socio-emotional domains. However, DQ can track the risk of ECD delay.

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FW: two issey's

This serves as a practical example of how media literacy can be applied to modern experiences, demonstrating the book's posthuman approach in action.

This quote highlights the importance of these interdisciplinary approaches in understanding complex human-technology relations.

This quote highlights the shift from interdisciplinary research, which draws from multiple fields, to a transdisciplinary solution that transcends disciplinary boundaries. It signifies a deeper synthesis where concepts from different fields merge to form a higher-level framework. All of this helps to illustrate the complexity of understanding human-technology interactions.

This quote emphasizes the limitations of language and the conveying of ideas.

This quote discusses how technology has evolved from being something external to something integrated into our daily lives. It suggests that media is now deeply embedded in how we perceive and interact with the world, blurring the lines between reality and virtual experiences.

This quote emphasizes how sociocultural factors like power and language and how they help shape our identity. This quote highlights that we are influenced not just by technology, but by the broader societal context in which we live in. The complexity of how media content and media context intertwine is still a challenge for researchers.

Postphenomenology specifically analyzes how humans are always in a relationship with one another and the world. Technology affects not only how we interact but how we perceive the world that we live in.

Messages containing underlying stereotypes are everywhere on digital social media platforms. Teaching people how to interact, understand, empathize, and navigate in our digital world is crucial. People take and change the meaning of different concepts and what they view online, and it becomes a negative concept for people seeking entertainment online.

The significance of being taught to fully understand the effects media technologies have on humans is crucial and is highlighted throughout this section.

This states the impact that digital technologies have on us. The Center for Humane Technology warns us how technologies are seeking attention on numerous platforms which in the end, harms society.

The authors approach to research

Postphenomenology (new vocabulary word I learned) - the author researches the mediating relations between humans and technologies. Postphenomenology is a term used to describe the authors approach to the philosophy of technology in the digital age we live in.

eLife Assessment

This study investigates the role of the Cadherin Flamingo (Fmi) in cell competition in developing tissues in Drosophila melanogaster. The findings are valuable in that they show that Fmi is required in winning cells in several competitive contexts. The evidence supporting the conclusions is solid, as the authors identify Fmi as a potential new regulator of cell competition, however, they don't delve into a mechanistic understanding of how this occurs.

Reviewer #1 (Public review):

Summary:

This paper is focused on the role of Cadherin Flamingo (Fmi) in cell competition in developing Drosophila tissues. A primary genetic tool is monitoring tissue overgrowths caused by making clones in the eye disc that expression activated Ras (RasV12) and that are depleted for the polarity gene scribble (scrib). The main system that they use is ey-flp, which make continuous clones in the developing eye-antennal disc beginning at the earliest stages of disc development. It should be noted that RasV12, scrib-i (or lgl-i) clones only lead to tumors/overgrowths when generated by continuous clones, which presumably creates a privileged environment that insulates them from competition. Discrete (hs-flp) RasV12, lgl-i clones are in fact out-competed (PMID: 20679206), which is something to bear in mind. They assess the role of fmi in several kinds of winners, and their data support the conclusion that fmi is required for winner status. However, they make the claim that loss of fmi from Myc winners converts them to losers, and the data supporting this conclusion is not compelling.

Strengths:

Fmi has been studied for its role in planar cell polarity, and its potential role in competition is interesting.

Weaknesses:<br /> I have read the revised manuscript and have found issues that need to be resolved. The biggest concern is the overstatement of the results that loss of fmi from Myc-overexpressing clones turns them into losers. This is not shown in a compelling manner in the revised manuscript and the authors need to tone down their language or perform more experiments to support their claims. Additionally, the data about apoptosis is not sufficiently explained.

Reviewer #2 (Public review):

Summary:<br /> In this manuscript, Bosch et al. reveal Flamingo (Fmi), a planar cell polarity (PCP) protein, is essential for maintaining 'winner' cells in cell competition, using Drosophila imaginal epithelia as a model. They argue that tumor growth induced by scrib-RNAi and RasV12 competition is slowed by Fmi depletion. This effect is unique to Fmi, not seen with other PCP proteins. Additional cell competition models are applied to further confirm Fmi's role in 'winner' cells. The authors also show that Fmi's role in cell competition is separate from its function in PCP formation.

Strengths:

(1) The identification of Fmi as a potential regulator of cell competition under various conditions is interesting.<br /> (2) The authors demonstrate that the involvement of Fmi in cell competition is distinct from its role in planar cell polarity (PCP) development.

Weaknesses:

(1) The authors provide a superficial description of the related phenotypes, lacking a mechanistic understanding of how Fmi regulates cell competition. While induction of apoptosis and JNK activation are commonly observed outcomes in various cell competition conditions, it is crucial to determine the specific mechanisms through which they are induced in fmi-depleted clones. Furthermore, it is recommended that the authors utilize the power of fly genetics to conduct a series of genetic epistasis analyses.

Reviewer #3 (Public review):

Summary:

In this manuscript, Bosch and colleagues describe an unexpected function of Flamingo, a core component of the planar cell polarity pathway, in cell competition in Drosophila wing and eye disc. While Flamingo depletion has no impact on tumour growth (upon induction of Ras and depletion of Scribble throughout the eye disc), and no impact when depleted in WT cells, it specifically tunes down winner clone expansion in various genetic contexts, including the overexpression of Myc, the combination of Scribble depletion with activation of Ras in clones or the early clonal depletion of Scribble in eye disc. Flamingo depletion reduces proliferation rate and increases the rate of apoptosis in the winner clones, hence reducing their competitiveness up to forcing their full elimination (hence becoming now "loser"). This function of Flamingo in cell competition is specific of Flamingo as it cannot be recapitulated with other components of the PCP pathway, does not rely on interaction of Flamingo in trans, nor on the presence of its cadherin domain. Thus, this function is likely to rely on a non-canonical function of Flamingo which may rely on downstream GPCR signaling.

This unexpected function of Flamingo is by itself very interesting. In the framework of cell competition, these results are also important as they describe, to my knowledge, one of the only genetic conditions that specifically affect the winner cells without any impact when depleted in the loser cells. Moreover, Flamingo do not just suppress the competitive advantage of winner clones, but even turn them in putative losers. This specificity, while not clearly understood at this stage, opens a lot of exciting mechanistic questions, but also a very interesting long term avenue for therapeutic purpose as targeting Flamingo should then affect very specifically the putative winner/oncogenic clones without any impact in WT cells.

The data and the demonstration are very clean and compelling, with all the appropriate controls, proper quantifications and backed-up by observations in various tissues and genetic backgrounds. I don't see any weakness in the demonstration and all the points raised and claimed by the authors are all very well substantiated by the data. As such, I don't have any suggestions to reinforce the demonstration.

While not necessary for the demonstration, documenting the subcellular localisation and levels of Flamingo in these different competition scenarios may have been relevant and provide some hints on a putative mechanism (specifically by comparing its localisation in winner and loser cells).

Also, on a more interpretative note, the absence of impact of Flamingo depletion on JNK activation does not exclude some interesting genetic interactions. JNK output can be very contextual (for instance depending on Hippo pathway status), and it would be interesting in the future to check if Flamingo depletion could somehow alter the effect of JNK in the winner cells and promote downstream activation of apoptosis (which might normally be suppressed). It would be interesting to check if Flamingo depletion could have an impact in other contexts involving JNK activation or upon mild activation of JNK in clones.

Strengths:

- A clean and compelling demonstration of the function of Flamingo in winner cells during cell competition

- One of the rare genetic conditions that affects very specifically winner cells without any impact in losers, and then can completely switch the outcome of competition (which opens an interesting therapeutic perspective on the long term)

Weaknesses:

- The mechanistic understanding obviously remains quite limited at this stage especially since the signaling does not go through the PCP pathway.

Author response:

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

Reviewer 1:

Summary: 

This paper is focused on the role of Cadherin Flamingo (Fmi) - also called Starry night (stan) - in cell competition in developing Drosophila tissues. A primary genetic tool is monitoring tissue overgrowths caused by making clones in the eye disc that express activated Ras (RasV12) and that are depleted for the polarity gene scribble (scrib). The main system that they use is ey-flp, which makes continuous clones in the developing eye-antennal disc beginning at the earliest stages of disc development. It should be noted that RasV12, scrib-i (or lgl-i) clones only lead to tumors/overgrowths when generated by continuous clones, which presumably creates a privileged environment that insulates them from competition. Discrete (hs-flp) RasV12, lgl-i clones are in fact outcompeted (PMID: 20679206), which is something to bear in mind. 

We think it is unlikely that the outcome of RasV12, scrib (or lgl) competition depends on discrete vs. continuous clones or on creation of a privileged environment. As shown in the same reference mentioned by the reviewer, the outcome of RasV12, scrib (or lgl) tumors greatly depends on the clone being able to grow to a certain size. The authors show instances of discrete clones where larger RasV12, lgl clones outcompete the surrounding tissue and eliminate WT cells by apoptosis, whereas smaller clones behave more like losers. It is not clear what aspect of the environment determines the ability of some clones to grow larger than others, but in neither case are the clones prevented from competition. Other studies show that in mammalian cells, RasV12, scrib clones are capable of outcompeting the surrounding tissue, such as in Kohashi et al (2021), where cells carrying both mutations actively eliminate their neighbors.

The authors show that clonal loss of Fmi by an allele or by RNAi in the RasV12, scrib-i tumors suppresses their growth in both the eye disc (continuous clones) and wing disc (discrete clones). The authors attributed this result to less killing of WT neighbors when Myc over-expressing clones lacking Fmi, but another interpretation (that Fmi regulates clonal growth) is equally as plausible with the current results. 

See point (1) for a discussion on this.

Next, the authors show that scrib-RNAi clones that are normally out-competed by WT cells prior to adult stages are present in higher numbers when WT cells are depleted for Fmi. They then examine death in RasV12, scrib-i ey-FLP clones, or in discrete hsFLP UAS-Myc clones. They state that they see death in WT cells neighboring RasV12, scrib-i clones in the eye disc (Figures 4A-C). Next, they write that RasV12, scrib-I cells become losers (i.e., have apoptosis markers) when Fmi is removed. Neither of these results are quantified and thus are not compelling. They state that a similar result is observed for Myc over-expression clones that lack Fmi, but the image was not compelling, the results are not quantified and the controls are missing (Myc over-expressing clones alone and Fmi clones alone). 

We assayed apoptosis in UAS-Myc clones in eye discs but neglected to include the results in Figure 4. We include them in the updated manuscript. Regarding Fmi clones alone, we direct the reviewer’s attention to Fig. 2 Supplement 1 where we showed that fminull clones cause no competition. Dcp-1 staining showed low levels of apoptosis unrelated to the fminull clones or twin-spots.

Regarding the quantification of apoptosis, we did not provide a quantification, in part because we observe a very clear visual difference between groups (Fig. 4A-K), and in part because it is challenging to come up with a rigorous quantification method. For example, how far from a winner clone can an apoptotic cell be and still be considered responsive to the clone? For UASMyc winner clones, we observe a modest amount of cell death both inside and outside the clones, consistent with prior observations. For fminull UAS-Myc clones, we observe vastly more cell death within the fminull UAS-Myc clones and modest death in nearby wildtype cells, and consequently a much higher ratio of cell death inside vs outside the clone. Because of the somewhat arbitrary nature of quantification, and the dramatic difference, we initially chose not to provide a quantification. However, given the request, we chose an arbitrary distance from the clone boundary in which to consider dying cells and counted the numbers for each condition. We view this as a very soft quantification, but we nevertheless report it in a way that captures the phenomenon in the revised manuscript. 

They then want to test whether Myc over-expressing clones have more proliferation. They show an image of a wing disc that has many small Myc overexpressing clones with and without Fmi. The pHH3 results support their conclusion that Myc overexpressing clones have more pHH3, but I have reservations about the many clones in these panels (Figures 5L-N). 

As the reviewer’s reservations are not specified, we have no specific response.

They show that the cell competition roles of Fmi are not shared by another PCP component and are not due to the Cadherin domain of Fmi. The authors appear to interpret their results as Fmi is required for winner status. Overall, some of these results are potentially interesting and at least partially supported by the data, but others are not supported by the data.

Strengths: 

Fmi has been studied for its role in planar cell polarity, and its potential role in competition is interesting.

Weaknesses:

(1) In the Myc over-expression experiments, the increased size of the Myc clones could be because they divide faster (but don't outcompete WT neighbors). If the authors want to conclude that the bigger size of the Myc clones is due to out-competition of WT neighbors, they should measure cell death across many discs of with these clones. They should also assess if reducing apoptosis (like using one copy of the H99 deficiency that removes hid, rpr, and grim) suppresses winner clone size. If cell death is not addressed experimentally and quantified rigorously, then their results could be explained by faster division of Myc over-expressing clones (and not death of neighbors). This could also apply to the RasV12, scrib-i results.

Indeed, Myc clones have been shown to divide faster than WT neighbors, but that is not the only reason clones are bigger. As shown in (de la Cova et al, 2004), Myc-overexpressing cells induce apoptosis in WT neighbors, and blocking this apoptosis results in larger wings due to increased presence of WT cells. Also, (Moreno and Basler, 2004) showed that Myc-overexpressing clones cause a reduction in WT clone size, as WT twin spots adjacent to 4xMyc clones are significantly smaller than WT twin spots adjacent to WT clones. In the same work, they show complete elimination of WT clones generated in a tub-Myc background. Since then, multiple papers have shown these same results. It is well established then that increased cell proliferation transforms Myc clones into supercompetitors and that in the absence of cell competition, Myc-overexpressing discs produce instead wings larger than usual. 

In (de la Cova et al, 2004) the authors already showed that blocking apoptosis with H99 hinders competition and causes wings with Myc clones to be larger than those where apoptosis wasn’t blocked. As these results are well established from prior literature, there is no need to repeat them here. 

(2) This same comment about Fmi affecting clone growth should be considered in the scrib RNAi clones in Figure 3.

In later stages, scrib RNAi clones in the eye are eliminated by WT cells. While scrib RNAi clones are not substantially smaller in third instar when competing against fmi cells (Fig 3M), by adulthood we see that WT clones lacking Fmi have failed to remove scrib clones, unlike WT clones that have completely eliminated the scrib RNAi clones by this time. We therefore disagree that the only effect of Fmi could be related to rate of cell division. 

(3) I don't understand why the quantifications of clone areas in Figures 2D, 2H, 6D are log values. The simple ratio of GFP/RFP should be shown. Additionally, in some of the samples (e.g., fmiE59 >> Myc, only 5 discs and fmiE59 vs >Myc only 4 discs are quantified but other samples have more than 10 discs). I suggest that the authors increase the number of discs that they count in each genotype to at least 20 and then standardize this number.

Log(ratio) values are easier to interpret than a linear scale. If represented linearly, 1 means equal ratios of A and B, while 2A/B is 2 and A/2B is 0.5. And the higher the ratio difference between A and B, the starker this effect becomes, making a linear scale deceiving to the eye, especially when decreased ratios are shown. Using log(ratios), a value of 0 means equal ratios, and increased and decreased ratios deviate equally from 0.

Statistically, either analyzing a standardized number of discs for all conditions or a variable number not determined beforehand has no effect on the p-value, as long as the variable n number is not manipulated by p-hacking techniques, such as increasing the n of samples until a significant p-value has been obtained. While some of our groups have lower numbers, all statistical analyses were performed after all samples were collected. For all results obtained by cell counts, all samples had a minimum of 10 discs due to the inherent though modest variability of our automated cell counts, and we analyzed all the discs that we obtained from a given experiment, never “cherry-picking” examples. For the sake of transparency, all our graphs show individual values in addition to the distributions so that the reader knows the n values at a glance.

(5) Figure 4 - shows examples of cell death. Cas3 is written on the figure but Dcp-1 is written in the results. Which antibody was used? The authors need to quantify these results. They also need to show that the death of cells is part of the phenotype, like an H99 deficiency, etc (see above).

Thank you for flagging this error. We used cleaved Dcp-1 staining to detect cell death, not Cas3 (Drice in Drosophila). We updated all panels replacing Cas3 by Dcp-1. 

As described above, cell death is a well established consequence of myc overexpression induced cell death and we feel there is no need to repeat that result. To what extent loss of Fmi induces excess cell death or reduces proliferation in “would-be” winners, and to what extent it reduces “would-be” winners’ ability to eliminate competitors are interesting mechanistic questions that are beyond the scope of the current manuscript.

(6) It is well established that clones overexpressing Myc have increased cell death. The authors should consider this when interpreting their results.

We are aware that Myc-overexpressing clones have increased cell death, but it has also been demonstrated that despite that fact, they behave as winners and eliminate WT neighboring cells. And as mentioned in comment (1), WT clones generated in a 3x and 4x Myc background are eliminated and removed from the tissue, and blocking cell death increases the size of WT “losers” clones adjacent to Myc overexpressing clones. 

(7) A better characterization of discrete Fmi clones would also be helpful. I suggest inducing hs-flp clones in the eye or wing disc and then determining clone size vs twin spot size and also examining cell death etc. If such experiments have already been done and published, the authors should include a description of such work in the preprint.

We have already analyzed the size of discrete Fmi clones and showed that they did not cause any competition, with fmi-null clones having the same size as WT clones in both eye and wing discs. We direct the reviewer’s attention to Figure 2 Supplement 1.

(8) We need more information about the expression pattern of Fmi. Is it expressed in all cells in imaginal discs? Are there any patterns of expression during larval and pupal development? 

Fmi is equally expressed by all cells in all imaginal discs in Drosophila larva and pupa. We include this information and the relevant reference (Brown et al, 2014) in the updated manuscript.

(9) Overall, the paper is written for specialists who work in cell competition and is fairly difficult to follow, and I suggest re-writing the results to make it accessible to a broader audience.

We have endeavored to both provide an accessible narrative and also describe in sufficient detail the data from multiple models of competition and complex genetic systems. We hope that most readers will be able, at a minimum, to follow our interpretations and the key takeaways, while those wishing to examine the nuts and bolts of the argument will find what they need presented as simply as possible.

Reviewer 2:

Summary: 

In this manuscript, Bosch et al. reveal Flamingo (Fmi), a planar cell polarity (PCP) protein, is essential for maintaining 'winner' cells in cell competition, using Drosophila imaginal epithelia as a model. They argue that tumor growth induced by scrib-RNAi and RasV12 competition is slowed by Fmi depletion. This effect is unique to Fmi, not seen with other PCP proteins. Additional cell competition models are applied to further confirm Fmi's role in 'winner' cells. The authors also show that Fmi's role in cell competition is separate from its function in PCP formation.

We would like to thank the reviewer for their thoughtful and positive review.

Strengths:

(1) The identification of Fmi as a potential regulator of cell competition under various conditions is interesting.

(2) The authors demonstrate that the involvement of Fmi in cell competition is distinct from its role in planar cell polarity (PCP) development.

Weaknesses:

(1) The authors provide a superficial description of the related phenotypes, lacking a comprehensive mechanistic understanding. Induction of apoptosis and JNK activation are general outcomes, but it is important to determine how they are specifically induced in Fmi-depleted clones. The authors should take advantage of the power of fly genetics and conduct a series of genetic epistasis analyses.

We appreciate that this manuscript does not address the mechanism by which Fmi participates in cell competition. Our intent here is to demonstrate that Fmi is a key contributor to competition. We indeed aim to delve into mechanism, are currently directing our efforts to exploring how Fmi regulates competition, but the size of the project and required experiments are outside of the scope of this manuscript. We feel that our current findings are sufficiently valuable to merit sharing while we continue to investigate the mechanism linking Fmi to competition. 

(2) The depletion of Fmi may not have had a significant impact on cell competition; instead, it is more likely to have solely facilitated the induction of apoptosis.

We respectfully disagree for several reasons. First, loss of Fmi is specific to winners; loss of Fmi has no effect on its own or in losers when confronting winners in competition. And in the Ras V12 tumor model, loss of Fmi did not perturb whole eye tumors – it only impaired tumor growth when tumors were confronted with competitors. We agree that induction of apoptosis is affected, but so too is proliferation, and only when in winners in competition.

(3) To make a solid conclusion for Figure 1, the authors should investigate whether complete removal of Fmi by a mutant allele affects tumor growth induced by expressing RasV12 and scrib RNAi throughout the eye.

We agree with the reviewer that this is a worthwhile experiment, given that RNAi has its limitations. However, as fmi is homozygous lethal at the embryo stage, one cannot create whole disc tumors mutant for fmi. As an approximation to this condition, we have introduced the GMR-Hid, cell-lethal combination to eliminate non-tumor tissue in the eye disc. Following elimination of non-tumor cells, there remains essentially a whole disc harboring fminull tumor. Indeed, this shows that whole fminull tumors overgrow similar to control tumors, confirming that the lack of Fmi only affects clonal tumors. We provide those results in the updated manuscript (Figure 1 Suppl 2 C-D).

(4) The authors should test whether the expression level of Fmi (both mRNA and protein) changes during tumorigenesis and cell competition.

This is an intriguing point that we considered worthwhile to examine. We performed immunostaining for Fmi in clones to determine whether its levels change during competition. Fmi is expressed ubiquitously at apical plasma membranes throughout the disc, and this was unchanged by competition, including inside >>Myc clones and at the clone boundary, where competition is actively happening. We provide these results as a new supplementary figure (Figure 5 Suppl 1) in the updated manuscript.

Reviewer 3:

Summary: 

In this manuscript, Bosch and colleagues describe an unexpected function of Flamingo, a core component of the planar cell polarity pathway, in cell competition in the Drosophila wing and eye disc. While Flamingo depletion has no impact on tumour growth (upon induction of Ras and depletion of Scribble throughout the eye disc), and no impact when depleted in WT cells, it specifically tunes down winner clone expansion in various genetic contexts, including the overexpression of Myc, the combination of Scribble depletion with activation of Ras in clones or the early clonal depletion of Scribble in eye disc. Flamingo depletion reduces the proliferation rate and increases the rate of apoptosis in the winner clones, hence reducing their competitiveness up to forcing their full elimination (hence becoming now "loser"). This function of Flamingo in cell competition is specific to Flamingo as it cannot be recapitulated with other components of the PCP pathway, and does not rely on the interaction of Flamingo in trans, nor on the presence of its cadherin domain. Thus, this function is likely to rely on a non-canonical function of Flamingo which may rely on downstream GPCR signaling.

This unexpected function of Flamingo is by itself very interesting. In the framework of cell competition, these results are also important as they describe, to my knowledge, one of the only genetic conditions that specifically affect the winner cells without any impact when depleted in the loser cells. Moreover, Flamingo does not just suppress the competitive advantage of winner clones, but even turns them into putative losers. This specificity, while not clearly understood at this stage, opens a lot of exciting mechanistic questions, but also a very interesting long-term avenue for therapeutic purposes as targeting Flamingo should then affect very specifically the putative winner/oncogenic clones without any impact in WT cells.

The data and the demonstration are very clean and compelling, with all the appropriate controls, proper quantification, and backed-up by observations in various tissues and genetic backgrounds. I don't see any weakness in the demonstration and all the points raised and claimed by the authors are all very well substantiated by the data. As such, I don't have any suggestions to reinforce the demonstration.

While not necessary for the demonstration, documenting the subcellular localisation and levels of Flamingo in these different competition scenarios may have been relevant and provided some hints on the putative mechanism (specifically by comparing its localisation in winner and loser cells). 

Also, on a more interpretative note, the absence of the impact of Flamingo depletion on JNK activation does not exclude some interesting genetic interactions. JNK output can be very contextual (for instance depending on Hippo pathway status), and it would be interesting in the future to check if Flamingo depletion could somehow alter the effect of JNK in the winner cells and promote downstream activation of apoptosis (which might normally be suppressed). It would be interesting to check if Flamingo depletion could have an impact in other contexts involving JNK activation or upon mild activation of JNK in clones.

We would like to thank the reviewer for their thorough and positive review.

Strengths: 

- A clean and compelling demonstration of the function of Flamingo in winner cells during cell competition.

- One of the rare genetic conditions that affects very specifically winner cells without any impact on losers, and then can completely switch the outcome of competition (which opens an interesting therapeutic perspective in the long term)

Weaknesses: 

- The mechanistic understanding obviously remains quite limited at this stage especially since the signaling does not go through the PCP pathway.

Reviewer 2 made the same comment in their weakness (1), and we refer to that response. In future work, we are excited to better understand the pathways linking Fmi and competition.

Author response:

Reviewer #2 (Public Review):

M. El Amri et al., investigated the functions of Marcks and Marcks like 1 during spinal cord (SC) development and regeneration in Xenopus laevis. The authors rigorously performed loss of function with morpholino knock-down and CRISPR knock-out combining rescue experiments in developing spinal cord in embryo and regeneration in tadpole stage.

For the assays in the developing spinal cord, a unilateral approach (knock-down/out only one side of the embryo) allowed the authors to assess the gene functions by direct comparing one-side (e.g. mutated SC) to the other (e.g. wild type SC on the other side). For the assays in regenerating SC, the authors microinject CRISPR reagents into 1-cell stage embryo. When the embryo (F0 crispants) grew up to tadpole (stage 50), the SC was transected. They then assessed neurite outgrowth and progenitor cell proliferation. The validation of the phenotypes was mostly based on the quantification of immunostaining images (neurite outgrowth: acetylated tubulin, neural progenitor: sox2, sox3, proliferation: EdU, PH3), that are simple but robust enough to support their conclusions. In both SC development and regeneration, the authors found that Marcks and Marcksl1 were necessary for neurite outgrowth and neural progenitor cell proliferation.

The authors performed rescue experiments on morpholino knock-down and CRISPR knock-out conditions by Marcks and Marcksl1 mRNA injection for SC development and pharmacological treatments for SC development and regeneration. The unilateral mRNA injection rescued the loss-of-function phenotype in the developing SC. To explore the signalling role of these molecules, they rescued the loss-of-function animals by pharmacological reagents They used S1P: PLD activator, FIPI: PLD inhibitor, NMI: PIP2 synthesis activator and ISA-2011B: PIP2 synthesis inhibitor. The authors found the activator treatment rescued neurite outgrowth and progenitor cell proliferation in loss of function conditions. From these results, the authors proposed PIP2 and PLD are the mediators of Marcks and Marcksl1 for neurite outgrowth and progenitor cell proliferation during SC development and regeneration. The results of the rescue experiments are particularly important to assess gene functions in loss of function assays, therefore, the conclusions are solid. In addition, they performed gain-of-function assays by unilateral Marcks or Marcksl1 mRNA injection showing that the injected side of the SC had more neurite outgrowth and proliferative progenitors. The conclusions are consistent with the loss-of-function phenotypes and the rescue results. Importantly, the authors showed the linkage of the phenotype and functional recovery by behavioral testing, that clearly showed the crispants with SC injury swam less distance than wild types with SC injury at 10-day post surgery.

Prior to the functional assays, the authors analyzed the expression pattern of the genes by in situ hybridization and immunostaining in developing embryo and regenerating SC. They confirmed that the amount of protein expression was significantly reduced in the loss of function samples by immunostaining with the specific antibodies that they made for Marcks and Marcksl1. Although the expression patterns are mostly known in previous works during embryo genesis, the data provided appropriate information to readers about the expression and showed efficiency of the knock-out as well.

MARCKS family genes have been known to be expressed in the nervous system. However, few studies focus on the function in nerves. This research introduced these genes as new players during SC development and regeneration. These findings could attract broader interests from the people in nervous disease model and medical field. Although it is a typical requirement for loss of function assays in Xenopus laevis, I believe that the efficient knock-out for four genes by CRISPR/Cas9 was derived from their dedication of designing, testing and validation of the gRNAs and is exemplary.

Weaknesses,

(1) Why did the authors choose Marcks and Marcksl1? The authors mentioned that these genes were identified with a recent proteomic analysis of comparing SC regenerative tadpole and non-regenerative froglet (Line (L) 54-57). However, although it seems the proteomic analysis was their own dataset, the authors did not mention any details to select promising genes for the functional assays (this article). In the proteomic analysis, there must be other candidate genes that might be more likely factors related to SC development and regeneration based on previous studies, but it was unclear what the criteria to select Marcks and Marcksl1 was.

To highlight the rationale for selecting these proteins, we reworded the sentence as follows: “A recent proteomic screen … after SCI identified a number of proteins that are highly upregulated at the tadpole stage but downregulated in froglets (Kshirsagar, 2020). These proteins included Marcks and Marcksl1, which had previously been implicated in the regeneration of other tissues (El Amri et al., 2018) suggesting a potential role for these proteins also in spinal cord regeneration.”

(2) Gene knock-out experiments with F0 crispants,

The authors described that they designed and tested 18 sgRNAs to find the most efficient and consistent gRNA (L191-195). However, it cannot guarantee the same phenotypes practically, due to, for example, different injection timing, different strains of Xenopus laevis, etc. Although the authors mentioned the concerns of mosaicism by themselves (L180-181, L289-292) and immunostaining results nicely showed uniformly reduced Marcks and Marcksl1 expression in the crispants, they did not refer to this issue explicitly.

To address this issue, we state explicitly in line 208-212: “We also confirmed by immunohistochemistry that co-injection of marcks.L/S and marcksl1.L/S sgRNA, which is predicted to edit all four homeologs (henceforth denoted as 4M CRISPR) drastically reduced immunostaining for Marcks and Marcksl1 protein on the injected side (Fig. S6 B-G), indicating that protein levels are reduced in gene-edited embryos.”

(3) Limitations of pharmacological compound rescue

In the methods part, the authors describe that they performed titration experiments for the drugs (L702-704), that is a minimal requirement for this type of assay. However, it is known that a well characterized drug is applied, if it is used in different concentrations, the drug could target different molecules (Gujral TS et al., 2014 PNAS). Therefore, it is difficult to eliminate possibilities of side effects and off targets by testing only a few compounds.

As explained in the responses to reviewer 1, we have completely rewritten and toned down our presentation of the pharmacological result and explicitly mention in our discussion now the possibility of side effects.

I am wondering the implication on secondary level & core course setting.

Author response:

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

Public Reviews:

Reviewer #2 (Public review):

Summary:

This work by Grogan and colleagues aimed to translate animal studies showing that acetylcholine plays a role in motivation by modulating the effects of dopamine on motivation. They tested this hypothesis with a placebo-controlled pharmacological study administering a muscarinic antagonist (trihexyphenidyl; THP) to a sample of 20 adult men performing an incentivized saccade task while undergoing electroencephalography (EEG). They found that reward increased vigor and reduced reaction times (RTs) and, importantly, these reward effects were attenuated by trihexyphenidyl. High incentives increased preparatory EEG activity (contingent negative variation), and though THP also increased preparatory activity, it also reduced this reward effect on RTs.

Strengths:

The researchers address a timely and potentially clinically relevant question with a within-subject pharmacological intervention and a strong task design. The results highlight the importance of the interplay between dopamine and other neurotransmitter systems in reward sensitivity and even though no Parkinson's patients were included in this study, the results could have consequences for patients with motivational deficits and apathy if validated in the future.

Weaknesses:

The main weakness of the study is the small sample size (N=20) that unfortunately is limited to men only. Generalizability and replicability of the conclusions remain to be assessed in future research with a larger and more diverse sample size and potentially a clinically relevant population. The EEG results do not shape a concrete mechanism of action of the drug on reward sensitivity.

We thank the reviewer for their time and their assessment of this manuscript, and we appreciate their helpful comments on the previous version.

We agree that the sample size being smaller than planned due to the pandemic restrictions is a weakness for this study, and hope that future studies into cholinergic effects on motivation in humans will use larger sample sizes. They should also ensure women are not excluded from sample populations, which will become even more important if the research progresses to clinical populations.

Reviewer #3 (Public review):

Summary:

Grogan et al examine a role for muscarinic receptor activation in action vigor in a saccadic system. This work is motivated by a strong literature linking dopamine to vigor, and some animal studies suggesting that ACH might modulate these effects, and is important because patient populations with symptoms related to reduced vigor are prescribed muscarinic antagonists. The authors use a motivated saccade task with distractors to measure the speed and vigor of actions in humans under placebo or muscarinic antagonism. They show that muscarinic antagonism blunts the motivational effects of reward on both saccade velocity and RT, and also modulates the distractibility of participants, in particular by increasing the repulsion of saccades away from distractors. They show that preparatory EEG signals reflect both motivation and drug condition, and make a case that these EEG signals mediate the effects of the drug on behavior.

Strengths:

This manuscript addresses an interesting and timely question and does so using an impressive within subject pharmacological design and a task well designed to measure constructs of interest. The authors show clear causal evidence that ACH affects different metrics of saccade generation related to effort expenditure and their modulation by incentive manipulations. The authors link these behavioral effects to motor preparatory signatures, indexed with EEG, that relate to behavioral measures of interest and in at least one case statistically mediate the behavioral effects of ACH antagonism.

Weaknesses:

A primary weakness of this paper is the sample size - since only 20 participants completed the study. The authors address the sample size in several places and I completely understand the reason for the reduced sample size (study halt due to covid). Nonetheless, it is worth stating explicitly that this sample size is relatively small for the effect sizes typically observed in such studies highlighting the need for future confirmatory studies.

We thank the reviewer for their time and their assessment of this manuscript, and we appreciate their helpful comments on the previous version.

We agree that the small sample size is a weakness of the study, and hope that future work into cholinergic modulation of motivation can involve larger samples to replicate and extend this work.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Thank you for addressing my comments and clarifying the analysis sections. Women can be included in such studies by performing a pregnancy test before each test session, but I understand how this could have added to the pandemic limitations. Best of luck with your future work!

Thank you for your time in reviewing this paper, and your helpful comments.

Reviewer #3 (Recommendations for the authors):

The authors have done a great job at addressing my concerns and I think that the manuscript is now very solid. That said, I have one minor concern.

Thank you for your time in reviewing this paper, and your helpful comments.

For descriptions of mass univariate analyses and cluster correction, I am still a bit confused on exactly what terms were in the regression. In one place, the authors state:

On each iteration we shuffled the voltages across trials within each condition and person, and regressed it against the behavioural variable, with the model 'variable ~1 + voltage + incentive*distractorPresent*THP + (1 | participant)'.

I take this to mean that the regression model includes a voltage regressor and a three-way interaction term, along with participant level intercept terms.

However, elsewhere, the authors state:

"We regressed each electrode and time-point against the three behavioural variables separately, while controlling for effects of incentive, distractor, THP, the interactions of those factors, and a random effect of participant."

I take this to mean that the regression model included regressors for incentive, distractorPresent, THP, along with their 2 and 3 way interactions. I think that this seems like the more reasonable model - but I just want to 1) verify that this is what the authors did and 2) encourage them to articulate this more clearly and consistently throughout.

We apologise for the lack of clarity about the whole-brain regression analyses.

We used Wilkinson notation for this formula, where ‘A*B’ denotes ‘A + B + A:B’, so all main effects and lower-order interactions terms were included in the regression, as your second interpretation says. The model written out in full would be:

'variable ~1 + voltage + incentive + distractorPresent + THP + incentive*distractorPresent + incentive*THP + distractorPresent*THP +  incentive*distractorPresent*THP + (1 | participant)'    

We will clarify this in the Version of Record.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors used a motivated saccade task with distractors to measure response vigor and reaction time (RT) in healthy human males under placebo or muscarinic antagonism. They also simultaneously recorded neural activity using EEG with event-related potential (ERP) focused analyses. This study provides evidence that the muscarinic antagonist Trihexyphenidyl (THP) modulates the motivational effects of reward on both saccade velocity and RT, and also increases the distractibility of participants. The study also examined the correlational relationships between reaction time and vigor and manipulations (THP, incentives) with components of the EEG-derived ERPs. While an interesting correlation structure emerged from the analyses relating the ERP biomarkers to behavior, it is unclear how these potentially epiphenomenal biomarkers relate to relevant underlying neurophysiology.

Strengths:

This study is a logical translational extension from preclinical findings of cholinergic modulation of motivation and vigor and the CNV biomarker to a normative human population, utilizing a placebo-controlled, double-blind approach.

While framed in the context of Parkinson's disease where cholinergic medications can be used, the authors do a good job in the discussion describing the limitations in generalizing their findings obtained in a normative and non-age-matched cohort to an aged PD patient population.

The exploratory analyses suggest alternative brain targets and/or ERP components that relate to the behavior and manipulations tested. These will need to be further validated in an adequately powered study. Once validated, the most relevant biomarkers could be assessed in a more clinically relevant population.

Weaknesses:

The relatively weak correlations between the main experimental outcomes provide unclear insight into the neural mechanisms by which the manipulations lead to behavioral manifestations outside the context of the ERP. It would have been interesting to evaluate how other quantifications of the EEG signal through time-frequency analyses relate to the behavioral outcomes and manipulations.

The ERP correlations to relevant behavioral outcomes were not consistent across manipulations demonstrating they are not reliable biomarkers to behavior but do suggest that multiple underlying mechanisms can give rise to the same changes in the ERP-based biomarkers and lead to different behavioral outcomes.

We thank the reviewer for their review and their comments.

We agree that these ERPs may not be reliable biomarkers yet, given the many-to-one mapping we observed where incentives and THP antagonism both affected the CNV in different ways, and hope that future studies will help clarify the use and limitations of the CNV as a potential biomarker of invigoration.

Our original hypothesis was specifically about the CNV as an index of preparatory behaviour, but we plan to look at potential changes to frequency characteristics in future work. We have included this in the discussion of future investigations. (page 16, line 428):

“Future investigations of other aspects of the EEG signals may illuminate us. Such studies could also investigate other potential signals that may be more sensitive to invigoration and/or muscarinic antagonism, including frequency-band power and phase-coherence, or measures of variability in brain signals such as entropy, which may give greater insight into processes affected by these factors.”

Reviewer #2 (Public Review):

Summary:

This work by Grogan and colleagues aimed to translate animal studies showing that acetylcholine plays a role in motivation by modulating the effects of dopamine on motivation. They tested this hypothesis with a placebo-controlled pharmacological study administering a muscarinic antagonist (trihexyphenidyl; THP) to a sample of 20 adult men performing an incentivized saccade task while undergoing electroengephalography (EEG). They found that reward increased vigor and reduced reaction times (RTs) and, importantly, these reward effects were attenuated by trihexyphenidyl. High incentives increased preparatory EEG activity (contingent negative variation), and though THP also increased preparatory activity, it also reduced this reward effect on RTs.

Strengths:

The researchers address a timely and potentially clinically relevant question with a within-subject pharmacological intervention and a strong task design. The results highlight the importance of the interplay between dopamine and other neurotransmitter systems in reward sensitivity and even though no Parkinson's patients were included in this study, the results could have consequences for patients with motivational deficits and apathy if validated in the future.

Weaknesses:

The main weakness of the study is the small sample size (N=20) that unfortunately is limited to men only. The generalizability and replicability of the conclusions remain to be assessed in future research with a larger and more diverse sample size and potentially a clinically relevant population. The EEG results do not shape a concrete mechanism of action of the drug on reward sensitivity.

We thank the reviewer for their review, and their comments.

We agree that our study was underpowered, not reaching our target of 27 participants due to pandemic restrictions halting our recruitment, and hope that future studies into muscarinic antagonism in motivation will have larger sample sizes, and include male and female participants across a range of ages, to assess generalisability.

We only included men to prevent the chance of administering the drug to someone pregnant. Trihexyphenidyl is categorized by the FDA as a Pregnancy Category Class C drug, and the ‘Summary of Product Characteristics’ states: “There is inadequate information regarding the use of trihexyphenidyl in pregnancy. Animal studies are insufficient with regard to effects on pregnancy, embryonal/foetal development, parturition and postnatal development. The potential risk for humans is unknown. Trihexyphenidyl should not be used during pregnancy unless clearly necessary.”

While the drug can be prescribed where benefits may outweigh this risk, as there were no benefits to participants in this study, we only recruited men to keep the risk at zero.

We have updated the Methods/Drugs section to explain this (page 17, line 494):

“The risks of Trihexyphenidyl in pregnancy are unknown, but the Summary Product of Characteristics states that it “should not be used during pregnancy unless clearly necessary”. As this was a basic research study with no immediate clinical applications, there was no justification for any risk of administering the drug during pregnancy, so we only recruited male participants to keep this risk at zero.”

And we reference to this in the Methods/Participants section (page 18, line 501):

“We recruited 27 male participants (see Drugs section above),…”

We agree that future work is needed to replicate this in different samples, and that this work cannot tell us the mechanism by which the drug is dampening invigoration, but we think that showing these effects do occur and can be linked to anticipatory/preparatory activity rather than overall reward sensitivity is a useful finding.

Reviewer #3 (Public Review):

Summary:

Grogan et al examine a role for muscarinic receptor activation in action vigor in a saccadic system. This work is motivated by a strong literature linking dopamine to vigor, and some animal studies suggesting that ACH might modulate these effects, and is important because patient populations with symptoms related to reduced vigor are prescribed muscarinic antagonists. The authors use a motivated saccade task with distractors to measure the speed and vigor of actions in humans under placebo or muscarinic antagonism. They show that muscarinic antagonism blunts the motivational effects of reward on both saccade velocity and RT, and also modulates the distractibility of participants, in particular by increasing the repulsion of saccades away from distractors. They show that preparatory EEG signals reflect both motivation and drug condition, and make a case that these EEG signals mediate the effects of the drug on behavior.

Strengths:

This manuscript addresses an interesting and timely question and does so using an impressive within-subject pharmacological design and a task well-designed to measure constructs of interest. The authors show clear causal evidence that ACH affects different metrics of saccade generation related to effort expenditure and their modulation by incentive manipulations. The authors link these behavioral effects to motor preparatory signatures, indexed with EEG, that relate to behavioral measures of interest and in at least one case statistically mediate the behavioral effects of ACH antagonism.

Weaknesses:

In full disclosure, I have previously reviewed this manuscript in another journal and the authors have done a considerable amount of work to address my previous concerns. However, I have a few remaining concerns that affect my interpretation of the current manuscript.

Some of the EEG signals (figures 4A&C) have profiles that look like they could have ocular, rather than central nervous, origins. Given that this is an eye movement task, it would be useful if the authors could provide some evidence that these signals are truly related to brain activity and not driven by ocular muscles, either in response to explicit motor effects (ie. Blinks) or in preparation for an upcoming saccade.

We thank the reviewer for re-reviewing the manuscript and for raising this issue.

All the EEG analyses (both ERP and whole-brain) are analysing the preparation period between the ready-cue and target appearance when no eye-movements are required. We reject trials with blinks or saccades over 1 degree in size, as detected by the Eyelink software according the sensitive velocity and acceleration criteria specified in the manuscript (Methods/Eye-tracking, page 19, line 550). This means that there should be no overt eye movements in the data. However, microsaccades and ocular drift are still possible within this period, which indeed could drive some effects. To measure this, we counted the number of microsaccades (<1 degree in size) in the preparation period between incentive cue and the target onset, for each trial. Further, we measure the mean absolute speed of the eye during the preparation period (excluding the periods during microsaccades) for each trial.

We have run a control analysis to check whether including ocular drift speed or number of microsaccades as a covariate in the whole-brain regression analysis changes the association between EEG and the behavioural metrics at frontal or other electrodes. Below we show these ‘variable ~ EEG’ beta-coefficients when controlling for each eye-movement covariate, in the same format as Figure 4. We did not run the permutation testing on this due to time/computational costs (it takes >1 week per variable), so p-values were not calculated, only the beta-coefficients. The beta-coefficients are almost unchanged, both in time-course and topography, when controlling for either covariate.  The frontal associations to velocity and distractor pull remain, suggesting they are not due to these eye movements.

We have added this figure as a supplemental figure.

For additional clarity in this response, we also plot the differences between these covariate-controlled beta-coefficients, and the true beta-coefficients from figure 4 (please note the y-axis scales are -0.02:0.02, not -0.15:0.15 as in Figure 4 and Figure 4-figure supplement 2). This shows that the changes to the associations between EEG and velocity/distractor-pull were not frontally-distributed, demonstrating eye-movements were not driving these effects. Relatedly, the RT effect’s change was frontally-distributed, despite Figure 4 showing the true relationship was central in focus, again indicating that effect was also not related to these eye movements.

Author response image 1.

Difference in beta-coefficients when eye-movement covariates are included. This is the difference from the beta-coefficients shown in Figure 4, please note the smaller y-axis limits.

The same pattern was seen if we controlled for the change in eye-position from the baseline period (measured by the eye-tracker) at each specific time-point, i.e., controlling for the distance the eye had moved from baseline at the time the EEG voltage is measured. The topographies and time-course plots were almost identical to the above ones:

Author response image 2.

Controlling for change in eye-position at each time-point does not change the regression results. Left column shows the beta-coefficients between the variable and EEG voltage, and the right column shows the difference from the main results in Figure 4 (note the smaller y-axis limits for the right-hand column).

Therefore, we believe the brain-behaviour regressions are independent of eye-movements. We have included the first figure presented here as an additional supplemental figure, and added the following to the text (page 10, line 265):

“An additional control analysis found that these results were not driven by microsaccades or ocular drift during the preparation period, as including these as trial-wise covariates did not substantially change the beta-coefficients (Figure 4 – Figure Supplement 2).”

For other EEG signals, in particular, the ones reported in Figure 3, it would be nice to see what the spatial profiles actually look like - does the scalp topography match that expected for the signal of interest?

Yes, the CNV is a central negative potential peaking around Cz, while the P3a is slightly anterior to this (peaking between Cz and FCz). We have added the topographies to the main figure (see point below).

This is the topography of the mean CNV (1200:1500ms from the preparation cue onset), which is maximal over Cz, as expected.

The P3a’s topography (200:280ms after preparation cue) is maximal slightly anterior to Cz, between Cz and FCz.

A primary weakness of this paper is the sample size - since only 20 participants completed the study. The authors address the sample size in several places and I completely understand the reason for the reduced sample size (study halt due to COVID). That said, they only report the sample size in one place in the methods rather than through degrees of freedom in their statistical tests conducted throughout the results. In part because of this, I am not totally clear on whether the sample size for each analysis is the same - or whether participants were removed for specific analyses (ie. due to poor EEG recordings, for example).  

We apologise for the lack of clarity here. All 20 participants were included in all analyses, although the number of trials included differed between behavioural and EEG analyses. We only excluded trials with EEG artefacts from the EEG analyses, not from the purely behavioural analyses such as Figures 1&2, although trials with blinks/saccades were removed from behavioural analyses too. Removing the EEG artefactual trials from the behavioural analyses did not change the findings, despite the lower power. The degrees of freedom in the figure supplement tables are the total number of trials (less 8 fixed-effect terms) included in the single-trial / trial-wise regression analyses we used.

We have clarified this in the Methods/Analysis (page 20, line 602):

“Behavioural and EEG analysis included all 20 participants, although trials with EEG artefacts were included in the behavioural analyses (18585 trials in total) and not the EEG analyses (16627 trials in total), to increase power in the former. Removing these trials did not change the findings of the behavioural analyses.”

And we state the number of participants and trials in the start of the behavioural results (page 3, line 97):

“We used single-trial mixed-effects linear regression (20 participants, 18585 trials in total) to assess the effects of Incentive, Distractors, and THP, along with all the interactions of these (and a random-intercept per participant), on residual velocity and saccadic RT.”

and EEG results section (page 7, line 193):

“We used single-trial linear mixed-effects regression to see the effects of Incentive and THP on each ERP (20 participants, 16627 trials; Distractor was included too, along with all interactions, and a random intercept by participant).”

Beyond this point, but still related to the sample size, in some cases I worry that results are driven by a single subject. In particular, the interaction effect observed in Figure 1e seems like it would be highly sensitive to the single subject who shows a reverse incentive effect in the drug condition.

Repeating that analysis after removing the participant with the large increase in saccadic RT with incentives did not remove the incentive*THP interaction effect – although it did weaken slightly from (β = 0.0218, p = .0002) to  (β=0.0197, p=.0082). This is likely because that while that participant did have slower RTs for higher incentives on THP, they were also slower for higher incentives under placebo (and similarly for distractor present/absent), making them less of an outlier in terms of effects than in raw RT terms. Below is Author response image 3 the mean-figure without that participant, and Author response image 4 that participant shown separately.

Author response image 3.

Author response image 4.

There are not sufficient details on the cluster-based permutation testing to understand what the authors did or whether it is reasonable. What channels were included? What metric was computed per cluster? How was null distribution generated?

We apologise for not giving sufficient details of this, and have updated the Methods/Analysis section to include these details, along with a brief description in the Results section.

To clarify here, we adapted the DMGroppe Mass Univariate Testing toolbox to also run cluster-based permutation regressions to examine the relationship between the behavioural variables and the voltages at all EEG electrodes at each time point. On each iteration we shuffled the voltages across trials within each condition and person, and regressed it against the behavioural variable, with the model ‘variable ~1 + voltage + incentive*distractorPresent*THP + (1 | participant)’. The Voltage term measured the association between voltage and the behavioural variable, after controlling for effects of incentive*distractor*THP on behaviour – i.e. does adding the voltage at this time/channel explain additional variance in the variable not captured in our main behavioural analyses. By shuffling the voltages, we removed the relationship to the behavioural variable, to build the null distribution of t-statistics across electrodes and time-samples. We used the ‘cluster mass’ method (Bullmore et al., 1999; Groppe et al., 2011; Maris & Oostenveld, 2007) to build the null distribution of cluster mass (across times/channels per iteration), and calculated the p-value as the proportion of this distribution further from zero than the absolute true t-statistics (two-tailed test).

We have given greater detail for this in the Methods/Analysis section (page 20, line 614):

“We adapted this toolbox to also run cluster-based permutation regressions to examine the relationship between the behavioural variables and the voltages at all EEG electrodes at each time point. On each iteration we shuffled the voltages across trials within each condition and person, and regressed it against the behavioural variable, with the model ‘~1 + voltage + incentive*distractorPresent*THP + (1 | participant)’. The Voltage term measured the association between voltage and the behavioural variable, after controlling for effects of incentive*distractor*THP on behaviour. By shuffling the voltages, we removed the relationship to the behavioural variable, to build the null distribution of t-statistics across electrodes and time-samples. We used the ‘cluster mass’ method (Bullmore et al., 1999; Groppe et al., 2011; Maris & Oostenveld, 2007) to build the null distribution, and calculated the p-value as the proportion of this distribution further from zero than the true t-statistics (two-tailed test). Given the relatively small sample size here, these whole-brain analyses should not be taken as definitive.”

And we have added a brief explanation to the Results section also (page 9, line 246):

“We regressed each electrode and time-point against the three behavioural variables separately, while controlling for effects of incentive, distractor, THP, the interactions of those factors, and a random effect of participant. This analysis therefore asks whether trial-to-trial neural variability predicts behavioural variability. To assess significance, we used cluster-based permutation tests (DMGroppe Mass Univariate toolbox; Groppe, Urbach, & Kutas, 2011), shuffling the trials within each condition and person, and repeating it 2500 times, to build a null distribution of ‘cluster mass’ from the t-statistics (Bullmore et al., 1999; Maris & Oostenveld, 2007) which was used to calculate two-tailed p-values with a family-wise error rate (FWER) of .05 (see Methods/Analysis for details).”

The authors report that "muscarinic antagonism strengthened the P3a" - but I was unable to see this in the data plots. Perhaps it is because the variability related to individual differences obscures the conditional differences in the plots. In this case, event-related difference signals could be helpful to clarify the results.

We thank the reviewer for spotting this wording error, this should refer to the incentive effect weakening the P3a, as no other significant effects were found on the P3a, as stated correctly in the previous paragraph. We have corrected this in the manuscript (page 9, line 232):

“This suggests that while incentives strengthened the incentive-cue response and the CNV and weakened the P3a, muscarinic antagonism strengthened the CNV,”

The reviewer’s suggestion for difference plots is very valuable, and we have added these to Figure 3, as well as increasing the y-axis scale for figure 3c to show the incentives weakening the P3a more clearly, and adding the topographies suggested in an earlier comment. The difference waves for Incentive and THP effects show that both are decreasing voltage, albeit with slightly different onset times – Incentive starts earlier, thus weakening the positive P3a, while both strengthen the negative CNV. The Incentive effects within THP and Placebo separately illustrate the THP*Incentive interaction.

We have amended the Results text and figure (page 7, line 200):

“The subsequent CNV was strengthened (i.e. more negative; Figure 3d) by incentive (β = -.0928, p < .0001) and THP (β = -0.0502, p < .0001), with an interaction whereby THP decreased the incentive effect (β= 0.0172, p = .0213). Figure 3h shows the effects of Incentive and THP on the CNV separately, using difference waves, and Figure 3i shows the incentive effect grows more slowly in the THP condition than the Placebo condition.

For mediation analyses, it would be useful in the results section to have a much more detailed description of the regression results, rather than just reporting things in a binary did/did not mediate sort of way. Furthermore, the methods should also describe how mediation was tested statistically (ie. What is the null distribution that the difference in coefficients with/without moderator is tested against?).

We have added a more detailed explanation of how we investigated mediation and mediated moderation, and now report the mediation effects for all tests run and the permutation-test p-values.

We had been using the Baron & Kenny (1986) method, based on 4 tests outlined in the updated text below, which gives a single measure of change in absolute beta-coefficients when all the tests have been met, but without any indication of significance; any reduction found after meeting the other 3 tests indicates a partial mediation under this method. We now use permutation testing to generate a p-value for the likelihood of finding an equal or larger reduction in the absolute beta-coefficients if the CNV were not truly related to RT. This found that the CNV’s mediation of the Incentive effect on RT was highly significant, while the Mediated Moderation of CNV on THP*Incentive was weakly significant.

During this re-analysis, we noticed that we had different trial-numbers in the different regression models, as EEG-artefactual trials were not excluded from the behavioural-only model (‘RT ~ 1 + Incentive’). However, this causes issues with the permutation testing as we are shuffling the ERPs and need the same trials included in all the mixed-effects models. Therefore, we have redone these mediation analyses, including only the trials with valid ERP measures (i.e. no artefactual trials) in all models. This has changed the beta-coefficients we report, but not the findings or conclusions of the mediation analyses. We have updated the figure to have these new statistics.

We have updated the text to explain the methodology in the Results section (page 12, line 284):

“We have found that neural preparatory activity can predict residual velocity and RT, and is also affected by incentives and THP. Finally, we ask whether the neural activity can explain the effects of incentives and THP, through mediation analyses. We used the Baron & Kenny ( 1986) method to assess mediation (see Methods/Analysis for full details). This tests whether the significant Incentive effect on behaviour could be partially reduced (i.e., explained) by including the CNV as a mediator in a mixed-effects single-trial regression. We measured mediation as the reduction in (absolute) beta-coefficient for the incentive effect on behaviour when the CNV was included as a mediator (i.e., RT ~ 1 + Incentive + CNV + Incentive*CNV + (1 | participant)). This is a directional hypothesis of a reduced effect, and to assess significance we ran a permutation-test, shuffling the CNV within participants, and measuring the change in absolute beta-coefficient for the Incentive effect on behaviour. This generates a distribution of mediation effects where there is no relationship between CNV and RT on a trial (i.e., a null distribution). We ran 2500 permutations, and calculated the proportion with an equal or more negative change in absolute beta-coefficient, equivalent to a one-tailed test. We ran this mediation analysis separately for the two behavioural variables of RT and residual velocity, but not for distractor pull as it was not affected by incentive, so failed the assumptions of mediation analyses (Baron & Kenny, 1986; Muller et al., 2005). We took the mean CNV amplitude from 1200:1500ms as our Mediator.

Residual velocity passed all the assumption tests for Mediation analysis, but no significant mediation was found. That is, Incentive predicted velocity (β=0.1304, t(1,16476)=17.3280, p<.0001); Incentive predicted CNV (β=-0.9122, t(1,16476)=-12.1800, p<.0001); and CNV predicted velocity when included alongside Incentive (β=0.0015, t(1,16475)=1.9753, p=.0483). However, including CNV did not reduce the Incentive effect on velocity, and in fact strengthened it (β=0.1318, t(1,16475)=17.4380, p<.0001; change in absolute coefficient: Δβ=+0.0014). Since there was no mediation (reduction), we did not run permutation tests on this.

However, RT did show a significant mediation of the Incentive effect by CNV: Incentive predicted RT (β=-0.0868, t(1,16476)=-14.9330, p<.0001); Incentive predicted CNV (β=-0.9122, t(1,16476)=-12.1800, p<.0001); and CNV predicted RT when included alongside Incentive (β=0.0127, t(1,16475)=21.3160, p<.0001). The CNV mediated the effect of Incentive on RT, reducing the absolute beta-coefficient (β=-0.0752, t(1,16475)=-13.0570, p<.0001; change in absolute coefficient: Δβ= -0.0116). We assessed the significance of this change via permutation testing, shuffling the CNV across trials (within participants) and calculating the change in absolute beta-coefficient for the Incentive effect on RT when the permuted CNV was included as a mediator. We repeated this 2500 times to build a null distribution of Δβ, and calculated the proportion with equal or stronger reductions for a one-tailed p-value, which was highly significant (p<.0001). This suggests that the Incentive effect on RT is partially mediated by the CNV’s amplitude during the preparation period, and this is not the case for residual velocity.

We also investigated whether the CNV could explain the cholinergic reduction in motivation (THP*Incentive interaction) on RT – i.e., whether CNV mediation the THP moderation. We measured Mediated Moderation as suggested by Muller et al. (2005; see Methods/Analysis for full explanation): Incentive*THP was associated with RT (β=0.0222, t(1,16474)=3.8272, p=.0001); and Incentive*THP was associated with CNV (β=0.1619, t(1,16474)=2.1671, p=.0302); and CNV*THP was associated with RT (β=0.0014, t(1,16472)=2.4061, p=.0161). Mediated Moderation was measured by the change in absolute Incentive*THP effect when THP*CNV was included in the mixed-effects model (β=0.0214, t(1,16472)=3.7298, p=.0002; change in beta-coefficient: Δβ= -0.0008), and permutation-testing (permuting the CNV as above) found a significant effect (p=.0132). This indicates cholinergic blockade changes how incentives affect preparatory negativity, and how this negativity reflects RT, which can explain some of the reduced invigoration of RT. However, this was not observed for saccade velocity.

And we have updated the Methods/Analysis section with a more detailed explanation too (page 21, line 627):

“For the mediation analysis, we followed the 4-step process  (Baron & Kenny, 1986; Muller et al., 2005), which requires 4 tests be met for the outcome (behavioural variable, e.g. RT), mediator (ERP, e.g., CNV) and the treatment (Incentive):

(1) Outcome is significantly associated with the Treatment (RT ~ 1 + Incentive + (1 | participant))

(2) Mediator is significantly associated with the Treatment (ERP ~ 1 + Incentive + (1 | participant))

(3) Mediator is significantly associated with the Outcome (RT ~ 1 + Incentive + ERP + (1 | participant))

(4) And the inclusion of the Mediator reduces the association between the Treatment and Outcome (Incentive effect from model #3)

The mediation was measured by the reduction in the absolute standardised beta coefficient between incentive and behaviour when the ERP mediator was included (model #3 vs model #1 above). We used permutation-testing to quantify the likelihood of finding these mediations under the null hypothesis, achieved by shuffling the ERP across trials (within each participant) to remove any link between the ERP and behaviour. We repeated this 2500 times to build a null distribution of the change in absolute beta-coefficients for the RT ~ Incentive effect when this permuted mediator was included (model #3 vs model #1). We calculated a one-tailed p-value by finding the proportion of the null distribution that was equal or smaller than the true values (as Mediation is a one-tailed prediction).

Mediated moderation (Muller et al., 2005) was used to see whether the effect of THP (the Moderator) on behaviour is mediated by the ERP, with the following tests (after the previous Mediation tests were already satisfied):

(5) THP moderates the Incentive effect, via a significant Treatment*Moderator interaction on the Outcome (RT ~ 1 + Incentive + THP + Incentive*THP + (1 | participant))

(6) THP moderates the Incentive effect on the Mediator, via a Treatment*Moderator interaction on the Outcome (ERP ~ 1 + Incentive + THP + Incentive*THP + (1 | participant))

(7) THP’s moderation of the Incentive effect is mediated by the ERP, via a reduction in the association of Treatment*Moderator on the Outcome when the Treatment*Moderator interaction is included (RT ~ 1 + Incentive + THP + Incentive*THP + ERP + ERP*THP + (1 | participant)

Mediated moderation is measured as the reduction in absolute beta-coefficients for ‘RT ~ Incentive*THP’ between model #5 and #7, which captures how much of this interaction could be explained by including the Mediator*Moderator interaction (ERP*THP in model #7). We tested the significance of this with permutation testing as above, permuting the ERP across trials (within participants) 2500 times, and building a null distribution of the change in the absolute beta-coefficients for RT ~ Incentive*THP between models #7 and #5. We calculated a one-tailed p-value from the proportion of these that were equal or smaller than the true change.”

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

(1) The analysis section could benefit from greater detail. For example, how exactly did they assess that the effects of the drug on peak velocity and RT were driven by non-distracting trials? Ideally, for every outcome, the analysis approach used should be detailed and justified.

We apologise for the confusion from this. To clarify, we found a 2-way regression (incentive*THP) on both residual velocity and saccadic RT and this pattern was stronger in distractor-absent trials for residual velocity, and stronger in distractor-present trials for saccadic RT, as can be seen in Figure 1d&e. However, as there was no significant 3-way interaction (incentive*THP*distractor) for either metric, and the 2-way interaction effects were in the same direction in distractor present/absent trials for both metrics, we think these effects were relatively unaffected by distractor presence.

We have updated the Results section to make this clearer: (page 3, line 94):

We measured vigour as the residual peak velocity of saccades within each drug session (see Figure 1c & Methods/Eye-tracking), which is each trial’s deviation of velocity from the main sequence. This removes any overall effects of the drug on saccade velocity, while still allowing incentives and distractors to have different effects within each drug condition. We used single-trial mixed-effects linear regression (20 participants, 18585 trials in total) to assess the effects of Incentive, Distractors, and THP, along with all the interactions of these (and a random-intercept per participant), on residual velocity and saccadic RT. As predicted, residual peak velocity was increased by incentives (Figure 1d; β = 0.1266, p < .0001), while distractors slightly slowed residual velocity (β = -0.0158, p = .0294; see Figure 1 – Figure supplement 1 for full behavioural statistics). THP decreased the effect of incentives on velocity (incentive * THP: β = -0.0216, p = .0030), indicating that muscarinic blockade diminished motivation by incentives. Figure 1d shows that this effect was similar in distractor absent/present trials, although slightly stronger when the distractor was absent; the 3-way (distractor*incentive*THP) interaction was not significant (p > .05), suggesting that the distractor-present trials had the same effect but weaker (Figure 1d).

Saccadic RT (time to initiation of saccade) was slower when participants were given THP (β = 0.0244, p = < .0001), faster with incentives (Figure 1e; β = -0.0767, p < .0001), and slowed by distractors (β = 0.0358, p < .0001). Again, THP reduced the effects of incentives (incentive*THP: β = 0.0218, p = .0002). Figure 1e shows that this effect was similar in distractor absent/present trials, although slightly stronger when the distractor was present; as the 3-way (distractor*incentive*THP) interaction was not significant and the direction of effects was the same in the two, it suggests the effect was similar in both conditions. Additionally, the THP*Incentive interactions were correlated between saccadic RT and residual velocity at the participant level (Figure 1 – Figure supplement 2).

We have given more details of the analyses performed in the Methods section and the results, as requested by you and the other reviewers (page 20, line 602):

Behavioural and EEG analysis included all 20 participants, although trials with EEG artefacts were included in the behavioural analyses (18585 trials in total) and not the EEG analyses (16627 trials in total), to increase power in the former. Removing these trials did not change the findings of the behavioural analyses.

We used single-trial linear-mixed effects models to analyse our data, including participant as a random effect of intercept, with the formula ‘~1 + incentive*distractor*THP + (1 | participant)’. We z-scored all factors to give standardised beta coefficients.

For the difference-wave cluster-based permutation tests (Figure 3 – Figure supplement 4), we used the DMGroppe Mass Univariate toolbox (Groppe et al., 2011), with 2500 permutations, to control the family-wise error rate at 0.05. This was used for looking at difference waves to test the effects of incentive, THP, and the incentive*THP interaction (using difference of difference-waves), across all EEG electrodes.

We adapted this toolbox to also run cluster-based permutation regressions to examine the relationship between the behavioural variables and the voltages at all EEG electrodes at each time point. On each iteration we shuffled the voltages across trials within each condition and person, and regressed it against the behavioural variable, with the model ‘~1 + voltage + incentive*distractorPresent*THP + (1 | participant)’. The Voltage term measured the association between voltage and the behavioural variable, after controlling for effects of incentive*distractor*THP on behaviour. By shuffling the voltages, we removed the relationship to the behavioural variable, to build the null distribution of t-statistics across electrodes and time-samples. We used the ‘cluster mass’ method (Bullmore et al., 1999; Groppe et al., 2011; Maris & Oostenveld, 2007) to build the null distribution, and calculated the p-value as the proportion of this distribution further from zero than the true t-statistics (two-tailed test). Given the relatively small sample size here, these whole-brain analyses should not be taken as definitive.

For the mediation analysis, we followed the 4-step process  (Baron & Kenny, 1986; Muller et al., 2005), which requires 4 tests be met for the outcome (behavioural variable, e.g. RT), mediator (ERP, e.g., CNV) and the treatment (Incentive):

(1) Outcome is significantly associated with the Treatment (RT ~ 1 + Incentive + (1 | participant))

(2) Mediator is significantly associated with the Treatment (ERP ~ 1 + Incentive + (1 | participant))

(3) Mediator is significantly associated with the Outcome (RT ~ 1 + Incentive + ERP + (1 | participant))

(4) And the inclusion of the Mediator reduces the association between the Treatment and Outcome (Incentive effect from model #3)

The mediation was measured by the reduction in the absolute standardised beta coefficient between incentive and behaviour when the ERP mediator was included (model #3 vs model #1 above). We used permutation-testing to quantify the likelihood of finding these mediations under the null hypothesis, achieved by shuffling the ERP across trials (within each participant) to remove any link between the ERP and behaviour. We repeated this 2500 times to build a null distribution of the change in absolute beta-coefficients for the RT ~ Incentive effect when this permuted mediator was included (model #3 vs model #1). We calculated a one-tailed p-value by finding the proportion of the null distribution that was equal or more negative than the true value (as Mediation is a one-tailed prediction). For this mediation analysis, we only included trials with valid ERP measures, even for the models without the ERP included (e.g., model #1), to keep the trial-numbers and degrees of freedom the same.

Mediated moderation (Muller et al., 2005) was used to see whether the effect of THP (the Moderator) on behaviour is mediated by the ERP, with the following tests (after the previous Mediation tests were already satisfied):

(5) THP moderates the Incentive effect, via a significant Treatment*Moderator interaction on the Outcome (RT ~ 1 + Incentive + THP + Incentive*THP + (1 | participant))

(6) THP moderates the Incentive effect on the Mediator, via a Treatment*Moderator interaction on the Outcome (ERP ~ 1 + Incentive + THP + Incentive*THP + (1 | participant))

(7) THP’s moderation of the Incentive effect is mediated by the ERP, via a reduction in the association of Treatment*Moderator on the Outcome when the Treatment*Moderator interaction is included (RT ~ 1 + Incentive + THP + Incentive*THP + ERP + ERP*THP + (1 | participant)

Mediated moderation is measured as the reduction in absolute beta-coefficients for ‘RT ~ Incentive*THP’ between model #5 and #7, which captures how much of this interaction could be explained by including the Mediator*Moderator interaction (ERP*THP in model #7). We tested the significance of this with permutation testing as above, permuting the ERP across trials (within participants) 2500 times, and building a null distribution of the change in the absolute beta-coefficients for RT ~ Incentive*THP between models #7 and #5. We calculated a one-tailed p-value from the proportion of these that were equal or more negative than the true change.

(2) Please explain why only men were included in this study. We are all hoping that men-only research is a practice of the past.

We only included men to prevent any chance of administering the drug to someone pregnant. Trihexyphenidyl is categorized by the FDA as a Pregnancy Category Class C drug, and the ‘Summary of Product Characteristics’ states: “There is inadequate information regarding the use of trihexyphenidyl in pregnancy. Animal studies are insufficient with regard to effects on pregnancy, embryonal/foetal development, parturition and postnatal development. The potential risk for humans is unknown. Trihexyphenidyl should not be used during pregnancy unless clearly necessary.”

While the drug can be prescribed where benefits may outweigh this risk, as there were no benefits to participants in this study, we only recruited men to keep the risk at zero.

We have updated the Methods/Drugs section to explain this (page 17, line 494):

“The risks of Trihexyphenidyl in pregnancy are unknown, but the Summary Product of Characteristics states that it “should not be used during pregnancy unless clearly necessary”. As this was a basic research study with no immediate clinical applications, there was no justification for any risk of administering the drug during pregnancy, so we only recruited male participants to keep this risk at zero.”

And we have referenced this in the Methods/Participants section (page 18, line 501):

“Our sample size calculations suggested 27 participants would detect a 0.5 effect size with .05 sensitivity and .8 power. We recruited 27 male participants (see Drugs section above)”

(3) Please explain acronyms (eg EEG) when first used.

Thank you for pointing this out, we have explained EEG at first use in the abstract and the main text, along with FWER, M1r, and ERP which had also been missed at first use.

Reviewer #3 (Recommendations For The Authors):

The authors say: "Therefore, acetylcholine antagonism reduced the invigoration of saccades by incentives, and increased the pull of salient distractors. We next asked whether these effects were coupled with changes in preparatory neural activity." But I found this statement to be misleading since the primary effects of the drug seem to have been to decrease the frequency of distractor-repulsed saccades... so "decreased push" would probably be a better analogy than "increased pull".

Thank you for noticing this, we agree, and have changed this to (page 5, line 165):

“Therefore, acetylcholine antagonism reduced the invigoration of saccades by incentives, and decreased the repulsion of salient distractors. We next asked whether these effects were coupled with changes in preparatory neural activity.”

I don't see anything in EEG preprocessing about channel rejection and interpolation. Were these steps performed? There are very few results related to the full set of electrodes.

We did not reject or interpolate any channels, as visual inspection found no obvious outliers in terms of noisiness, and no channels had standard deviations (across time/trials) higher than our standard cutoff (of 80). The artefact rejection was applied across all EEG channels, so any trials with absolute voltages over 200uV in any channel were removed from the analysis. On average 104/120 trials were included (having passed this check, along with eye-movement artefact checks) per condition per person, and we have added the range of these, along with totals across conditions to the Analysis section and a statement about channel rejection/interpolation (page 20, line 588):

“Epochs were from -200:1500ms around the preparation cue onset, and were baselined to the 100ms before the preparation cue appeared. Visual inspection found no channels with outlying variance, so no channel rejection or interpolation was performed. We rejected trials from the EEG analyses where participants blinked or made saccades (according to EyeLink criteria above) during the epoch, or where EEG voltage in any channel was outside -200:200μV (muscle activity). On average 104/120 trials per condition per person were included (SD = 21, range = 21-120), and 831/960 trials in total per person (SD=160, range=313-954). A repeated-measures ANOVA found there were no significant differences in number of trials excluded for any condition (p > .2).”

eLife Assessment

The authors have reported an important study in which they use a double-blind design to explore pharmacological manipulations in the context of a behavioral task. While the sample size is small, the use of varied methodology, including electrophysiology, behavior, and pharmacology, makes this manuscript particularly notable. Overall, the findings are solid and motivate future explanations into the relationships between acetylcholine and motivation.

Reviewer #2 (Public review):

Summary:

This work by Grogan and colleagues aimed to translate animal studies showing that acetylcholine plays a role in motivation by modulating the effects of dopamine on motivation. They tested this hypothesis with a placebo-controlled pharmacological study administering a muscarinic antagonist (trihexyphenidyl; THP) to a sample of 20 adult men performing an incentivized saccade task while undergoing electroencephalography (EEG). They found that reward increased vigor and reduced reaction times (RTs) and, importantly, these reward effects were attenuated by trihexyphenidyl. High incentives increased preparatory EEG activity (contingent negative variation), and though THP also increased preparatory activity, it also reduced this reward effect on RTs.

Strengths:

The researchers address a timely and potentially clinically relevant question with a within-subject pharmacological intervention and a strong task design. The results highlight the importance of the interplay between dopamine and other neurotransmitter systems in reward sensitivity and even though no Parkinson's patients were included in this study, the results could have consequences for patients with motivational deficits and apathy if validated in the future.

Weaknesses:

The main weakness of the study is the small sample size (N=20) that unfortunately is limited to men only. Generalizability and replicability of the conclusions remain to be assessed in future research with a larger and more diverse sample size and potentially a clinically relevant population. The EEG results do not shape a concrete mechanism of action of the drug on reward sensitivity.

Reviewer #3 (Public review):

Summary:

Grogan et al examine a role for muscarinic receptor activation in action vigor in a saccadic system. This work is motivated by a strong literature linking dopamine to vigor, and some animal studies suggesting that ACH might modulate these effects, and is important because patient populations with symptoms related to reduced vigor are prescribed muscarinic antagonists. The authors use a motivated saccade task with distractors to measure the speed and vigor of actions in humans under placebo or muscarinic antagonism. They show that muscarinic antagonism blunts the motivational effects of reward on both saccade velocity and RT, and also modulates the distractibility of participants, in particular by increasing the repulsion of saccades away from distractors. They show that preparatory EEG signals reflect both motivation and drug condition, and make a case that these EEG signals mediate the effects of the drug on behavior.

Strengths:

This manuscript addresses an interesting and timely question and does so using an impressive within subject pharmacological design and a task well designed to measure constructs of interest. The authors show clear causal evidence that ACH affects different metrics of saccade generation related to effort expenditure and their modulation by incentive manipulations. The authors link these behavioral effects to motor preparatory signatures, indexed with EEG, that relate to behavioral measures of interest and in at least one case statistically mediate the behavioral effects of ACH antagonism.

Weaknesses:

A primary weakness of this paper is the sample size - since only 20 participants completed the study. The authors address the sample size in several places and I completely understand the reason for the reduced sample size (study halt due to covid). Nonetheless, it is worth stating explicitly that this sample size is relatively small for the effect sizes typically observed in such studies highlighting the need for future confirmatory studies.

time AS place

"decolonization is not a metaphor"

!

!!!

Behind the Hamline University Incident — with Erika López Prater and Christiane Gruber

Both Gruber and Prater are art historians.

the Greenhouse Gas Protocol, distinguishes between three levels: scope 1 concerns direct emissions, scope 2 concerns direct purchases of energy, and scope 3 concerns indirect emissions upstream and downstream of the supply chain.

implicit cser - Participating in the wider formal and informal institutions for society’s interests and concerns.

CAUSE - Screws are sometimes sent loose in the bag of kitchen fittings

SOLUTION - Full set of sink clips sent

CAUSE - Door was a shortage and was marked down on shortage board as it should be

SOLUTION - Door was delivered to the factory 2 days later

... an iterable. This includes sets, dictionaries (the iteration will yield keys, values or key-value-pairs), and generators (such as ranges)

Ranges do not technically store the items. Instead, a range generates them in the order that they are requested. This allows you to work through absurdly long ranges of numbers without having to keep each one in memory. On the flip side, accessing an element towards the end of a range may take much longer than accessing the same element in a list of the same length.

this is a sub-section of 3

I learned it only yesterday: please add the information to use Balena Etcher https://etcher.balena.io/. Many people use Rufus and that is known to create problems here and there

who approved the term "Veeam ISO"?

i'm surprised by this information . It's not common to see a gay pastor because the Bible see homosexuality as a sin

Its interesting to see how an event lead to a new wave . People started to fight for their rights as they should and they decided to express themselves . It was like a fresh air .

RealNVP에서 사용된 Affine coupling transformation이 non-volume preserving인 이유는 무엇인가요? 또한, 이 특성이 모델의 성능에 어떤 영향을 미치나요?

Normalizing Flows가 많은 수의 레이어를 가질 경우에는 추론하는데 걸리는 시간이 어떻게 되나요? 특히 실시간 추론이나 아주 큰 데이터셋에서는 성능 저하 문제가 생길 것 같은데 이는 어떻게 해결할 수 있을까요?

Difficile d'asserter. Je propose: "Notre supposition: Algie Martin Simons (1870 - 1950), historien, parmi les créateurs du Socialist Party of America en 1901, auteur de The American Farmer (1902) et de Social Forces in American History (1911). Nous n’avons pas retrouvé l’origine précise de cette citation."

Al Tabari connects this verse with the satanic verses.

改为“排放路径”

to make many different things work effectively as a whole 協調；使相配合

We need someone to coordinate the whole campaign. 我們需要有個人來協調整個活動。

(adj.) )modern, recent, or containing the latest information 現代的;最近的;包含最新資訊的

We work hard to keep our database up to date. 我們花了很大力氣不斷更新資料庫。

dealing with or treating the whole of something or someone and not just a part 整體的，全面的

Ecological problems usually require holistic solutions. 生態問題通常需要全面性的解決方法。

(verb) to give the main facts about something 略述，概括

At the interview she outlined what I would be doing. 面試時，她簡要介紹了我將要從事的工作。

*(noun) a description of the main facts about something 大綱，概要，提要

to think about what will happen in the future and plan for these events 朝前看，展望未來，作長遠打算

We are trying to look ahead and see what our options are. 我們想作些長遠打算，看看我們都有哪些選擇。

to go as far as or past the usual limit of something 到…的限度；超過…的限度；竭盡

*Being stretched means that we're being challenged and pushed outside of our comfort zones in a positive way.

* If jobs or tasks stretch you, they make you learn new things that use your skill and experience more than before. （工作或任務）使施展本領，對…具有挑戰性

My present job doesn't stretch me, so I'm looking for something more demanding. 我目前的工作不能讓我盡展所長，所以我在尋找更有挑戰性的工作。

* at a stretch 連續地，不間斷地

There's no way I could work for ten hours at a stretch. 要我連續工作十小時是不可能的。

?구글 '권한 부여' 서버는 아래 query string(검색어)들을 제공함.

체크

'웹 서버 애플리케이션'은 또 무슨 말?

유저 데이터 접근에 승인을 요구하려면, 유저를 Google's OAuth 2.0 서버로 리다이렉트 시켜라.

In my view, this controversy is emblematic of the deepening division between traditional French "values" and the demographic and economic consequences of today's global trends. As in many countries, France is struggling with the difficulties of maintaining democratic values of freedom and equality under the guise of "protecting its national security." Personally, I agree with the UN and France's high court that taking away anyone's individual and civil liberties is too high a price to pay for asserting a country's national identity.