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Related Experiment Video

Updated: Mar 14, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
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Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels.

Nicholas T Van Dam1, David O'Connor2, Enitan T Marcelle3

  • 1Center for the Developing Brain, Child Mind Institute; Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.

Biological Psychiatry
|September 27, 2016
PubMed
Summary
This summary is machine-generated.

Data-driven methods identified distinct behavioral and neurobiological subgroups, revealing variation even within healthy individuals. These findings enhance understanding of brain-behavior relationships beyond current diagnostic categories.

Keywords:
Hierarchical clusteringMultivariate distance matrix regressionPhenotypesPsychopathologyRDoCResting state fMRI

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Data Science

Background:

  • Current diagnostic categories do not fully capture behavioral and biological variation.
  • Data-driven approaches offer a way to identify more homogenous subgroups.
  • Understanding brain-behavior relationships requires accounting for this variation.

Purpose of the Study:

  • To apply data-driven methods to identify subgroups based on phenotypic measures.
  • To investigate the neurobiological underpinnings of these identified subgroups.
  • To explore variation within both typical and atypical functioning.

Main Methods:

  • Utilized a community-ascertained sample (N=347) with behavioral, psychiatric, and resting-state fMRI data.
  • Applied bootstrap-based exploratory factor analysis and hybrid hierarchical clustering to phenotypic data.
  • Compared adjacent groups using t tests, chi-square tests, and multivariate distance matrix regression for functional connectivity.

Main Results:

  • Factor analysis yielded six factors explaining significant variance.
  • Hierarchical clustering identified nested phenotypic communities (2, 4, and 8 groups).
  • Distinct adaptive (sensation-seeking vs. extraverted/emotionally stable) and maladaptive (internalizing vs. externalizing) groups were found, with significant functional connectivity differences.

Conclusions:

  • Data-driven approaches successfully identified clinically meaningful subgroups across a spectrum of functioning.
  • These methods captured significant behavioral and neurobiological variation, including within healthy individuals.
  • Findings support the utility of data-driven approaches for refining our understanding of brain-behavior relationships.