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, Oemer Faruk Oeztuerk, David Popovic, Paolo Enrico, Anne Ruef, Nadia Bieler, Elif Sarisik, Johanna Weiske, Mark Sen Dong, Dominic B. Dwyer, Lana Kambeitz-Ilankovic, Shalaila S. Haas, Alexandra Stainton, Stephan Ruhrmann, Katharine Chisholm, Joseph Kambeitz, Anita Riecher-Rössler, Rachel Upthegrove, Frauke Schultze-Lutter, Raimo K.R. SalokangasJarmo Hietala, Christos Pantelis, Rebekka Lencer, Eva Meisenzahl, Stephen J. Wood, Paolo Brambilla, Stefan Borgwardt, Peter Falkai, Linda A. Antonucci, Alessandro Bertolino, Peter Liddle, Nikolaos Koutsouleris*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 (BAC slow-5 = 73.2%, BAC slow-4 = 72.9%, BAC slow-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.

Original languageEnglish
Pages (from-to)1207-1217
Number of pages11
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Issue number12
Early online date19 Jun 2023
Publication statusPublished - Dec 2023

Bibliographical note

Copyright © 2023 Published by Elsevier Inc on behalf of Society of Biological Psychiatry. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License [].


  • Early recognition
  • Formal thought disorder
  • Neuroimaging
  • Predictive modeling
  • Recent-onset psychosis
  • Subtyping


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