Asynchronous neural maturation predicts transition to psychosis.
brain age
machine-learning
neurodevelopment
psychosis
voxel-based morphometry
Journal
Psychiatry and clinical neurosciences
ISSN: 1440-1819
Titre abrégé: Psychiatry Clin Neurosci
Pays: Australia
ID NLM: 9513551
Informations de publication
Date de publication:
30 Oct 2023
30 Oct 2023
Historique:
revised:
08
10
2023
received:
12
04
2023
accepted:
24
10
2023
pubmed:
31
10
2023
medline:
31
10
2023
entrez:
31
10
2023
Statut:
aheadofprint
Résumé
Neuroimaging-based machine-learning predictions of psychosis onset rely on the hypothesis that structural brain anomalies may reflect the underlying pathophysiology. Yet, current predictors remain difficult to interpret in light of brain structure. Here, we combined an advanced interpretable supervised algorithm and a model of neuroanatomical age to identify the level of brain maturation of the regions most predictive of psychosis. We used the voxel-based morphometry of a healthy control dataset (N = 2024) and a prospective longitudinal UHR cohort (N = 82), of which 27 developed psychosis after one year. In UHR, psychosis was predicted at one year using Elastic-Net-Total-Variation (Enet-TV) penalties within a five-fold cross-validation, providing an interpretable map of distinct predictive regions. Using both the whole brain and each predictive region separately, a brain age predictor was then built and validated in 1605 controls, externally tested in 419 controls from an independent cohort, and applied in UHR. Brain age gaps were computed as the difference between chronological and predicted age, providing a proxy of whole-brain and regional brain maturation. Psychosis prediction was performant with 80 ± 4% of area-under-curve and 69 ± 5% of balanced accuracy (P < 0.001), and mainly leveraged volumetric increases in the ventromedial prefrontal cortex and decreases in the left precentral gyrus and the right orbitofrontal cortex. These regions were predicted to have delayed and accelerated maturational patterns, respectively. By combining an interpretable supervised model of conversion to psychosis with a brain age predictor, we showed that inter-regional asynchronous brain maturation underlines the predictive signature of psychosis.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-18-RHUS-0014 (PsyCARE)
Organisme : Agence Nationale de la Recherche
ID : ANR-19-CHIA-0010-01 (Big2Small)
Organisme : Horizon 2020 Framework Programme
ID : H2020-SC1-2017,754907(RLiNK)
Organisme : Fondation Bettencourt Schueller, CCA INSERM Bettencourt
Organisme : Fondation pour la Recherche Médicale
ID : FDM201806006059
Organisme : Ministère des Affaires Sociales et de la Santé
ID : PHRC AOM07-118 (ICAAR)
Organisme : Ministère des Affaires Sociales et de la Santé
ID : PHRC AOM13-681 (START)
Informations de copyright
© 2023 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.
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