Using machine learning to derive neurobiological subtypes of general psychopathology in late childhood.
Year: 2024
Type: article
Study design: Observational Study
Abstract: Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses often do not adequately map onto neurobiological features. Alternatively, our study used structural imaging data and a semisupervised machine learning technique, heterogeneity through discriminative analysis, to identify neurobiological subtypes in 9- to 10-year-olds with high psychopathology endorsements (n = 9,027). Our model revealed two stable neurobiological subtypes (adjusted Rand index = 0.38). Subtype 1 showed smaller structural properties, elevated conduct problems and attention-deficit/hyperactivity disorder symptoms, and impaired cognitive performance compared to Subtype 2 and typically developing youth. Subtype 2 had larger structural properties, cognitive abilities comparable to typically developing youth, and elevated internalizing symptoms relative to Subtype 1 and typically developing youth. These subtypes remained stable in their neurobiological characteristics, cognitive ability, and associated psychopathology traits over time. Taken together, our data-driven approach uncovered evidence of neural heterogeneity as demonstrated by structural patterns that map onto divergent profiles of psychopathology symptoms and cognitive performance in youth. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
DOI: 10.1037/abn0000898
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Funders: National Science Foundation, Brain and Behavior Research Foundation, American Psychological Foundation, Children's Hospital of Philadelphia, University of Pennsylvania, National Alliance for Research on Schizophrenia and Depression, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute on Drug Abuse, National Center for Advancing Translational Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1020259976, G1058358382, G1092750628, G1436251778, G1510635755, G1569016918, G1586463187, G1672542724, G1742849658, G2003922037, G2150354811, EDU: Deploying and Evaluating Secure Programming Education in the IDE, G2715786902, G2735337927, G2782475662, G2844825193, G2947707207, G298833120, G2998093352, G311279662, G3166945029, G3305455594, G343603955, G3438786812, G3563224455, G3738900353, G3755973507, G378850153, G3992405086, G4066048162, G4142848390, G4180929035, G4241744443, Early exposures and neurodevelopmental, behavioral, and health outcomes, G4680818468, G469525291, G4902183580, G5002640097, G5061896360, G5081895789, G5139799579, Graduate Research Fellowship Program (GRFP), G540810554, Delineating neurobiological heterogeneity in internalizing symptoms using machine learning and deep phenotyping, G5723270564, G5792261523, G5976565686, G6188346138, G6373421114, G6399140587, G6653174320, G687916792, G6887863799, G6922259526, G6967829241, G6983123888, Life-Span Development of Normal and Abnormal Behavior, G7166423909, G7271102572, G7307553525, G7414483225, G7652160222, G771167098, The Vanderbilt Institute for Clinical and Translational Research (VICTR), G812393343, G8132297629, G815084465, G8281824583, Creating an adaptive screening tool for detecting neurocognitive deficits and psychopathology across the lifespan, G8486495155, G8606111478, G8679417706, G8762821959, G8865478933, G898378059, G9000006386, G9000008241, G9000034221, G9000042425, G9000046731, G9000058527, G9000061868, G9000080137, G9000108491, G9000112882, G9000113866, G9000163168, G9000202101, G9000205938, G9000241459, G9000286149, 9/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U MICHIGAN, 17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLA, 8/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT CHLA, 6/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT OHSU, 21/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT WUSTL, 2/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UMB, Advanced machine learning algorithms that integrate multi-modal neuroimaging to quantify the heterogeneity in Alzheimer's Disease, G923039674, G95833762
OpenAlex ID: W4403945281