TY - JOUR
T1 - Data-Driven Phenotypic Categorization for Neurobiological Analyses
T2 - Beyond DSM-5 Labels
AU - Van Dam, Nicholas T.
AU - O'Connor, David
AU - Marcelle, Enitan T.
AU - Ho, Erica J.
AU - Cameron Craddock, R.
AU - Tobe, Russell H.
AU - Gabbay, Vilma
AU - Hudziak, James J.
AU - Xavier Castellanos, F.
AU - Leventhal, Bennett L.
AU - Milham, Michael P.
N1 - Publisher Copyright:
© 2016 Society of Biological Psychiatry
PY - 2017/3/15
Y1 - 2017/3/15
N2 - 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.
AB - 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.
KW - Hierarchical clustering
KW - Multivariate distance matrix regression
KW - Phenotypes
KW - Psychopathology
KW - RDoC
KW - Resting state fMRI
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U2 - 10.1016/j.biopsych.2016.06.027
DO - 10.1016/j.biopsych.2016.06.027
M3 - Article
C2 - 27667698
AN - SCOPUS:84994423034
SN - 0006-3223
VL - 81
SP - 484
EP - 494
JO - Biological Psychiatry
JF - Biological Psychiatry
IS - 6
ER -