Using Brain-Based Phenotyping to Improve Discovery in Psychiatry

Carol A. Tamminga, Elena I. Ivleva

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Many lines of evidence from different laboratories are now joining the same chorus: that conventional psychiatric diagnoses of serious mental illness (SMI), when tested, do not show a common biology. The article by Wolfers et al1 notes that SMI diagnoses do not have strong biomarkers similar to those that are already increasingly valued in the rest of medicine and that could help define disease groups, select treatments, and mark clinical outcomes. The authors used innovative regional brain structure mapping on an individual basis in people with schizophrenia and bipolar disorder and developed voxel-by-voxel measures of individual brain structure deviations from a normative model. They calculated group-level volume values for brain regions in a usual fashion, and then they derived deviations from the normative model, voxelwise, for each participant and each diagnostic group. Their calculations of individualized voxel-wise maps of deviance from the normative model allow the comparison of this individual deviance within and across diagnostic groups. First, Wolfers et al1 report typical group-level outcomes, which show the usual kinds of changes reported in SMI: gray matter reductions in frontal, temporal, and cerebellar regions in people with schizophrenia and gray matter reductions in the frontal cortex in individuals with bipolar disorder. However, in a section that is to our knowledge unique to their analysis, the authors also demonstrated localizations of extreme individual deviations by voxel and by group. This study shows that, despite the mean disease-associated deviations, there are many individual volumetric deviations unmasked by these analyses that are not consistent within or across conventional diagnoses. The authors conclude that, based on this extreme heterogeneity, the DSM-defined categories of SMI are not useful for clustering biologically similar disorders. The authors suggest applying clustering algorithms to these deviations to find subtypes of disorders based on biology, a suggestion they have left to future researchers. The authors report being disappointed by the insufficiency of conventionaldiagnoses fordisease characterization.This study is a clear portrayal of extreme neurobiological heterogeneity across individuals within conventional SMI diagnoses.

Original languageEnglish (US)
JournalJAMA Psychiatry
DOIs
StateAccepted/In press - Jan 1 2018

ASJC Scopus subject areas

  • Psychiatry and Mental health

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