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Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels
Nicholas T Van Dam,
Enitan T. Marcelle,
Erica J. Ho,
Russell H Tobe,
James J Hudziak,
F Xavier Castellanos,
Bennett L. Leventhal,
Michael P. Milham
Posted 06 May 2016
bioRxiv DOI: 10.1101/051789 (published DOI: 10.1016/j.biopsych.2016.06.027)
Posted 06 May 2016
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, ages 18-59) completed a battery of behavioral measures, psychiatric assessment, and resting state functional magnetic resonance imaging (R-fMRI) 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 6 factors identified 2, 4, and 8 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 captured behavioral and neurobiological variation among healthy individuals as well.
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