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



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.



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.



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.
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.
Nicholas T
NT
Van Dam
Center for the Developing Brain, Child Mind Institute; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.
David
D
O'Connor
Center for the Developing Brain, Child Mind Institute; Department of Psychology, University of California, Berkeley, Berkeley.
Enitan T
ET
Marcelle
Center for the Developing Brain, Child Mind Institute.
Erica J
EJ
Ho
Center for the Developing Brain, Child Mind Institute; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.
R
R
Cameron Craddock
Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.
Russell H
RH
Tobe
Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai.
Vilma
V
Gabbay
The Child Study Center at NYU Langone Medical Center, New York.
James J
JJ
Hudziak
The Child Study Center at NYU Langone Medical Center, New York; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York.
F
F
Xavier Castellanos
Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York; Department of Psychiatry, University of California, San Francisco, San Francisco, California.
Bennett L
BL
Leventhal
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.
Michael P
MP
Milham
D000293:Adolescent; D000328:Adult; D001921:Brain / Q000503:physiopathology*; D016000:Cluster Analysis; D063132:Connectome; D003430:Cross-Sectional Studies; D039721:Diagnostic and Statistical Manual of Mental Disorders; D006801:Humans; D008279:Magnetic Resonance Imaging; D001523:Mental Disorders / Q000175:diagnosis* / Q000503:physiopathology; D008875:Middle Aged; D015999:Multivariate Analysis; D009483:Neuropsychological Tests; D010641:Phenotype; D055815:Young Adult
Hierarchical clustering; Multivariate distance matrix regression; Phenotypes; Psychopathology; RDoC; Resting state fMRI
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