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Neuroanatomical markers of psychotic experiences in adolescents: a machine-learning approach in a longitudinal population-based sample

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posted on 18.05.2022, 16:27 authored by Joanne PM Kenney, Laura Milena Rueda-Delgado, Erik O Hanlon, Lee Jollans, Ian KelleherIan Kelleher, Colm HealyColm Healy, Niamh DooleyNiamh Dooley, Conor McCandless, Thomas Frodl, Alexander Leemans, Catherine Lebel, Robert Whelan, Mary CannonMary Cannon
It is important to identify accurate markers of psychiatric illness to aid early prediction of disease course. Subclinical psychotic experiences (PEs) are important risk factors for later mental ill-health and suicidal behaviour. This study used machine learning to investigate neuroanatomical markers of PEs in early and later stages of adolescence. Machine learning using logistic regression using Elastic Net regularization was applied to T1-weighted and diffusion MRI data to classify adolescents with subclinical psychotic experiences vs. controls across 3 timepoints (Time 1:11–13 years, n = 77; Time 2:14–16 years, n = 56; Time 3:18–20 years, n = 40). Neuroimaging data classified adolescents aged 11–13 years with current PEs vs. controls returning an AROC of 0.62, significantly better than a null model, p = 1.73e-29. Neuroimaging data also classified those with PEs at 18–20 years (AROC = 0.59;P = 7.19e-10) but performance was at chance level at 14–16 years (AROC = 0.50). Left hemisphere frontal regions were top discriminant classifiers for 11–13 years-old adolescents with PEs, particularly pars opercularis. Those with future PEs at 18–20 years-old were best distinguished from controls based on left frontal regions, right-hemisphere medial lemniscus, cingulum bundle, precuneus and genu of the corpus callosum (CC). Deviations from normal adolescent brain development in young people with PEs included an acceleration in the typical pattern of reduction in left frontal thickness and right parietal curvature, and accelerated progression of microstructural changes in right white matter and corpus callosum. These results emphasise the importance of multi-modal analysis for understanding adolescent PEs and provide important new insights into early phenotypes for psychotic experiences.

Funding

European Research Council Consolidator Award (iHear 724809)

European Research Council and Irish Research Council (project number: GOIPD/2019/708)

History

Comments

The original article is available at https://www.sciencedirect.com/

Published Citation

Kenney JPM. et al. Neuroanatomical markers of psychotic experiences in adolescents: a machine-learning approach in a longitudinal population-based sample. Neuroimage Clin. 2022;34:102983

Publication Date

4 March 2022

PubMed ID

35287090

Department/Unit

  • Beaumont Hospital
  • Psychiatry

Research Area

  • Neurological and Psychiatric Disorders
  • Population Health and Health Services
  • Endocrinology

Publisher

Elsevier BV

Version

  • Published Version (Version of Record)