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Biol Psychiatry. 2017 Mar 15;81(6):484-494. doi: 10.1016/j.biopsych.2016.06.027. Epub 2016 Jul 19.

Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels.

Author information

1
Center 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.
2
Center for the Developing Brain, Child Mind Institute; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.
3
Center for the Developing Brain, Child Mind Institute; Department of Psychology, University of California, Berkeley, Berkeley.
4
Center for the Developing Brain, Child Mind Institute.
5
Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.
6
Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai.
7
The Child Study Center at NYU Langone Medical Center, New York.
8
The Child Study Center at NYU Langone Medical Center, New York; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.
9
Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York; Department of Psychiatry, University of California, San Francisco, San Francisco, California.
10
Center for the Developing Brain, Child Mind Institute; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York; Department of Psychiatry, University of Vermont, Burlington, Vermont. Electronic address: michael.milham@childmind.org.

Abstract

BACKGROUND:

Data-driven approaches can capture behavioral and biological variation currently unaccounted for by contemporary diagnostic categories, thereby enhancing the ability of neurobiological studies to characterize brain-behavior relationships.

METHODS:

A community-ascertained sample of individuals (N = 347, 18-59 years of age) completed a battery of behavioral measures, psychiatric assessment, and resting-state functional magnetic resonance imaging in a cross-sectional design. Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales from 10 measures. Hybrid hierarchical clustering was applied to resultant factor scores to identify nested groups. Adjacent groups were compared via independent samples t tests and chi-square tests of factor scores, syndrome scores, and psychiatric prevalence. Multivariate distance matrix regression examined functional connectome differences between adjacent groups.

RESULTS:

Reduction yielded six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained exploratory models, respectively. Hybrid hierarchical clustering of these six factors identified two, four, and eight nested groups (i.e., phenotypic communities). At the highest clustering level, the algorithm differentiated functionally adaptive and maladaptive groups. At the middle clustering level, groups were separated by problem type (maladaptive groups; internalizing vs. externalizing problems) and behavioral type (adaptive groups; sensation-seeking vs. extraverted/emotionally stable). Unique phenotypic profiles were also evident at the lowest clustering level. Group comparisons exhibited significant differences in intrinsic functional connectivity at the highest clustering level in somatomotor, thalamic, basal ganglia, and limbic networks.

CONCLUSIONS:

Data-driven approaches for identifying homogenous subgroups, spanning typical function to dysfunction, not only yielded clinically meaningful groups, but also captured behavioral and neurobiological variation among healthy individuals.

KEYWORDS:

Hierarchical clustering; Multivariate distance matrix regression; Phenotypes; Psychopathology; RDoC; Resting state fMRI

PMID:
27667698
PMCID:
PMC5402759
DOI:
10.1016/j.biopsych.2016.06.027
[Indexed for MEDLINE]
Free PMC Article

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