Brain-Predicted Age Associates With Psychopathology Dimensions in Youths

Vanessa L Cropley; Ye Tian; Kavisha Fernando; Sina Mansour L; Christos Pantelis; Luca Cocchi; Andrew Zalesky

doi:10.1016/j.bpsc.2020.07.014

Brain-Predicted Age Associates With Psychopathology Dimensions in Youths

Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Apr;6(4):410-419. doi: 10.1016/j.bpsc.2020.07.014. Epub 2020 Aug 1.

Authors

Vanessa L Cropley¹, Ye Tian², Kavisha Fernando², Sina Mansour L³, Christos Pantelis⁴, Luca Cocchi⁵, Andrew Zalesky³

Affiliations

¹ Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Victoria, Australia. Electronic address: vcropley@unimelb.edu.au.
² Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
³ Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia.
⁴ Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia.
⁵ QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

PMID: 32981878
DOI: 10.1016/j.bpsc.2020.07.014

Abstract

Background: This study aimed to investigate whether dimensional constructs of psychopathology relate to variation in patterns of brain development and to determine whether these constructs share common neurodevelopmental profiles.

Methods: Psychiatric symptom ratings from 9312 youths (8-21 years old) from the Philadelphia Neurodevelopmental Cohort were parsed into 7 independent dimensions of clinical psychopathology representing conduct, anxiety, obsessive-compulsive, attention, depression, bipolar, and psychosis symptoms. Using a subset of this cohort with structural magnetic resonance imaging (n = 1313), a normative model of brain morphology was established and the model was then applied to predict the age of youths with clinical symptoms. We investigated whether the deviation of brain-predicted age from true chronological age, called the brain age gap, explained individual variation in each psychopathology dimension.

Results: Individual variation in the brain age gap significantly associated with clinical dimensions representing psychosis (t = 3.16, p = .0016), obsessive-compulsive symptoms (t = 2.5, p = .01), and general psychopathology (t = 4.08, p < .0001). Greater symptom severity along these dimensions was associated with brain morphology that appeared older than expected for typically developing youths of the same age. Psychopathology dimensions clustered into 2 modules based on shared brain loci where putative accelerated neurodevelopment was most prominent. Patterns of morphological development were accelerated in frontal cortices for depression, psychosis, and conduct symptoms (module 1), whereas acceleration was most evident in subcortex and insula for the remaining dimensions (module 2).

Conclusions: Our findings suggest that increased brain age, particularly in frontal cortex and subcortical nuclei, underpins clinical psychosis and obsessive-compulsive symptoms in youths. Psychopathology dimensions share common neural substrates, despite representing clinically independent symptom profiles.

Keywords: Brain-predicted age; Development; Gray matter structure; Obsessive-compulsive symptoms; Psychopathology; Psychosis.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Brain
Cerebral Cortex
Child
Humans
Magnetic Resonance Imaging
Psychopathology*
Psychotic Disorders*
Young Adult