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. 2023 Dec;8(12):1207-1217.
doi: 10.1016/j.bpsc.2023.06.001. Epub 2023 Jun 19.

Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

Madalina-Octavia Buciuman  1 , Oemer Faruk Oeztuerk  1 , David Popovic  1 , Paolo Enrico  2 , Anne Ruef  3 , Nadia Bieler  3 , Elif Sarisik  1 , Johanna Weiske  3 , Mark Sen Dong  3 , Dominic B Dwyer  4 , Lana Kambeitz-Ilankovic  5 , Shalaila S Haas  6 , Alexandra Stainton  7 , Stephan Ruhrmann  5 , Katharine Chisholm  8 , Joseph Kambeitz  5 , Anita Riecher-Rössler  9 , Rachel Upthegrove  10 , Frauke Schultze-Lutter  11 , Raimo K R Salokangas  12 , Jarmo Hietala  12 , Christos Pantelis  13 , Rebekka Lencer  14 , Eva Meisenzahl  15 , Stephen J Wood  16 , Paolo Brambilla  17 , Stefan Borgwardt  18 , Peter Falkai  19 , Linda A Antonucci  20 , Alessandro Bertolino  21 , Peter Liddle  22 , Nikolaos Koutsouleris  23 ; PRONIA Consortium
Affiliations

Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

Madalina-Octavia Buciuman et al. Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Dec.

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: Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite 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 multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume 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 gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BACslow-5 = 73.2%, BACslow-4 = 72.9%, BACslow-3 = 68.0%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7%), and smaller frontal white matter volume (BAC = 73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77%.

Conclusions: We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and associated poor outcomes.

Keywords: Early recognition; Formal thought disorder; Neuroimaging; Predictive modeling; Recent-onset psychosis; Subtyping.

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