Using Machine Learning to Derive Neurobiological Subtypes of General Psychopathology in Late Childhood

Gabrielle E. Reimann, Randolph M. Dupont, Aristeidis Sotiras, Tom Earnest, Hee Jung Jeong, E. Leighton Durham, Camille Archer, Tyler M. Moore, Benjamin B. Lahey, Antonia N. Kaczkurkin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)647-655
Number of pages9
JournalJournal of Psychopathology and Clinical Science
Volume133
Issue number8
DOIs
StatePublished - 2024

Keywords

  • attention-deficit/hyperactivity disorder
  • conduct problems
  • general psychopathology
  • internalizing
  • machine learning subtypes

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