부산대학교 도서관

Aim: 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. Methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]