On 2015 Jan 30, Joe Newton commented:

This consortium study is unusually interesting because it now strongly suggest increased intracranial volume “differentials” in multiple diagnostic categories. (Newton JR Med Hypotheses Jan 1999)<br> A very large body of multidisciplinary evidence, I observed for over 50 years, when combined is consistent with increased volume differentials and other increased kinematic differentials for the “manic-depressive anomalies”. (Newton JR Med Hypotheses Jan 1999) I now call these anomalies because they are not always disabilities and diagnosis is uncertain. This 1999 testable hypothesis was followed by analyses of genes by many authors such as: Linked gene ontology categories are novel and differ from associated gene ontology categories for the bipolar disorders. Newton JR. Psychiatric Genetics. 2007 Feb;17(1):29-34. PMID: 17167342 Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Askland K, et al. Hum Genet. 2009 Feb;125(1):63-79. PMID: 9052778 Combinations of SNPs related to signal transduction in bipolar disorder. Koefoed P, et al. PLoS One. 2011;6(8):e23812. PMID: 21897858 Connection between genetic and clinical data in bipolar disorder. Mellerup E, et al. PLoS One. 2012;7(9):e44623. PMID: 23028568 Ion channels and schizophrenia: a gene set-based analytic approach to GWAS data for biological hypothesis testing. Askland K, et al. Hum Genet. 2012 Mar;131(3):373-91. PMID: 21866342 Convergent functional genomics of psychiatric disorders. Niculescu AB. Am J Med Genet B Neuropsychiatr Genet. 2013 Oct;162B(7):587-94. PMID: 23728881 The above data are consistent with thousands of linked/associated genes of low effect. These mainly influence structure suggesting intracranial neuron volume differentials (per Monro-Kelli) and/or myelination structure differentials. Further studies suggest that many of the linked/associated genes are shared by at least three DSM affective categories. (Genes swelling and shrinking: from edema to psychiatry. Newton JR in process) However, the science community has apparently not made the connection between action potential (AP) conduction velocity, AP event dyscoordination, and the state changes in the human information system anomalies.<br> The healthy living intracranial system with the lowest differentials in: volumes, axon calibers, myelinations, velocities and thus the lowest timing differentials accurately: coordinates, integrates, and binds all system AP events. By the same physics, in an unhealthy system, some AP events will be less coordinated and thus more uncertain. Diagnosis of the psychiatric disorder category is uncertain in the total absence of confirmed markers. For example, a diagnosis of “bipolar disorder” of an individual is based on the longitudinal response, e.g. state, which is very subjective as there are unknown numbers of possible states for any individual over time. Increased volume and/or myelination differentials are expected to be structural biomarkers for illness in a very broad range of diagnoses with gene variants in factorial numbers of particular additive combinations as suggested by several scientists, such as Mellerup and others. Such a combination expressed in an individual is expected to increase especially volume differentials due to the Monro-Kelli forces. It’s important to note that increased volume (or myelination) differentials may, or may not, be detectable in all patients with current imaging, and indeed, it’s physically expected that some patients with increased differentials within a ROI can have a normal overall volume of that measured ROI. The same is also very likely of myelination differentials. A consortium has the capability, if carefully managed by a few simple rules, to do truly novel and useful science. As suggested by the authors “selecting phenotypes in a principled way” is expected to be most useful. For example, selecting patients with abnormal hippocampal volumes selected for subsequent genotyping likely will to reveal the associated genetic loci, e.g. particular ROIs are expected. Particular variants are expected to most influence particular intracranial ROI volumes, and volumes are expected to be phenotypes or biomarkers in the psychiatric anomalies.

On 2015 Jan 30, Joe Newton commented:

This consortium study is unusually interesting because it now strongly suggest increased intracranial volume “differentials” in multiple diagnostic categories. (Newton JR Med Hypotheses Jan 1999)<br> A very large body of multidisciplinary evidence, I observed for over 50 years, when combined is consistent with increased volume differentials and other increased kinematic differentials for the “manic-depressive anomalies”. (Newton JR Med Hypotheses Jan 1999) I now call these anomalies because they are not always disabilities and diagnosis is uncertain. This 1999 testable hypothesis was followed by analyses of genes by many authors such as: Linked gene ontology categories are novel and differ from associated gene ontology categories for the bipolar disorders. Newton JR. Psychiatric Genetics. 2007 Feb;17(1):29-34. PMID: 17167342 Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Askland K, et al. Hum Genet. 2009 Feb;125(1):63-79. PMID: 9052778 Combinations of SNPs related to signal transduction in bipolar disorder. Koefoed P, et al. PLoS One. 2011;6(8):e23812. PMID: 21897858 Connection between genetic and clinical data in bipolar disorder. Mellerup E, et al. PLoS One. 2012;7(9):e44623. PMID: 23028568 Ion channels and schizophrenia: a gene set-based analytic approach to GWAS data for biological hypothesis testing. Askland K, et al. Hum Genet. 2012 Mar;131(3):373-91. PMID: 21866342 Convergent functional genomics of psychiatric disorders. Niculescu AB. Am J Med Genet B Neuropsychiatr Genet. 2013 Oct;162B(7):587-94. PMID: 23728881 The above data are consistent with thousands of linked/associated genes of low effect. These mainly influence structure suggesting intracranial neuron volume differentials (per Monro-Kelli) and/or myelination structure differentials. Further studies suggest that many of the linked/associated genes are shared by at least three DSM affective categories. (Genes swelling and shrinking: from edema to psychiatry. Newton JR in process) However, the science community has apparently not made the connection between action potential (AP) conduction velocity, AP event dyscoordination, and the state changes in the human information system anomalies.<br> The healthy living intracranial system with the lowest differentials in: volumes, axon calibers, myelinations, velocities and thus the lowest timing differentials accurately: coordinates, integrates, and binds all system AP events. By the same physics, in an unhealthy system, some AP events will be less coordinated and thus more uncertain. Diagnosis of the psychiatric disorder category is uncertain in the total absence of confirmed markers. For example, a diagnosis of “bipolar disorder” of an individual is based on the longitudinal response, e.g. state, which is very subjective as there are unknown numbers of possible states for any individual over time. Increased volume and/or myelination differentials are expected to be structural biomarkers for illness in a very broad range of diagnoses with gene variants in factorial numbers of particular additive combinations as suggested by several scientists, such as Mellerup and others. Such a combination expressed in an individual is expected to increase especially volume differentials due to the Monro-Kelli forces. It’s important to note that increased volume (or myelination) differentials may, or may not, be detectable in all patients with current imaging, and indeed, it’s physically expected that some patients with increased differentials within a ROI can have a normal overall volume of that measured ROI. The same is also very likely of myelination differentials. A consortium has the capability, if carefully managed by a few simple rules, to do truly novel and useful science. As suggested by the authors “selecting phenotypes in a principled way” is expected to be most useful. For example, selecting patients with abnormal hippocampal volumes selected for subsequent genotyping likely will to reveal the associated genetic loci, e.g. particular ROIs are expected. Particular variants are expected to most influence particular intracranial ROI volumes, and volumes are expected to be phenotypes or biomarkers in the psychiatric anomalies.