Structural and functional brain patterns predict formal thought disorder's severity and its persistence in recent-onset psychosis: Results from the PRONIA Study

Abstract

BACKGROUND: Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level. METHODS: 233 individuals with recent-onset psychosis were drawn from the multi-site European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multi-band fractional amplitude of low frequency fluctuations (fALFF), gray-matter volume (GMV) and white-matter volume (WMV) data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up. RESULTS: Cross-sectionally, multivariate patterns of GMV within the salience, dorsal attention, visual and ventral attention networks separated the FThD severity subgroups (BAC=60.8%). Longitudinally, distributed activations/deactivations within all fALFF sub-bands (BAC(slow-5)=73.2%, BAC(slow-4)=72.9%, BAC(slow-3)=68.0), GMV patterns overlapping with the cross-sectional ones (BAC=62.7%) and smaller frontal WMV (BAC=73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multi-modal balanced accuracy of BAC=77%. CONCLUSIONS: We report first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open the avenue for the development of neuroimaging-based diagnostic, prognostic and treatment options for the early recognition and management of FThD and associated poor outcomes.