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 (BACslow-5=73.2%, BACslow-4=72.9%, BACslow-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.
Structural and functional brain patterns predict formal thought disorder's severity and its persistence in recent-onset psychosis: Results from the PRONIA Study
Antonucci, Linda ASupervision
;Bertolino, AlessandroWriting – Review & Editing
;
2023-01-01
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 (BACslow-5=73.2%, BACslow-4=72.9%, BACslow-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.| File | Dimensione | Formato | |
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Buciuman2023.pdf
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Structural and Functional Brain Patterns Predict.pdf
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