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: 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.

Publication DOI: https://doi.org/10.1016/j.bpsc.2023.06.001
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences > Aston Institute of Health & Neurodevelopment (AIHN)
College of Health & Life Sciences
Additional Information: 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 [https://creativecommons.org/licenses/by-nc-nd/4.0/].
Uncontrolled Keywords: Early recognition,Formal thought disorder,Neuroimaging,Predictive modeling,Recent-onset psychosis,Subtyping,Clinical Neurology,Biological Psychiatry,Cognitive Neuroscience,Radiology Nuclear Medicine and imaging
Publication ISSN: 2451-9022
Last Modified: 16 May 2024 07:25
Date Deposited: 09 Aug 2023 10:21
Full Text Link:
Related URLs: https://www.sci ... 451902223001441 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-12
Published Online Date: 2023-06-19
Accepted Date: 2023-06-03
Authors: Buciuman, Madalina-Octavia
Oeztuerk, Oemer Faruk
Popovic, David
Enrico, Paolo
Ruef, Anne
Bieler, Nadia
Sarisik, Elif
Weiske, Johanna
Dong, Mark Sen
Dwyer, Dominic B.
Kambeitz-Ilankovic, Lana
Haas, Shalaila S.
Stainton, Alexandra
Ruhrmann, Stephan
Chisholm, Katharine (ORCID Profile 0000-0002-0575-0789)
Kambeitz, Joseph
Riecher-Rössler, Anita
Upthegrove, Rachel
Schultze-Lutter, Frauke
Salokangas, Raimo K.R.
Hietala, Jarmo
Pantelis, Christos
Lencer, Rebekka
Meisenzahl, Eva
Wood, Stephen J.
Brambilla, Paolo
Borgwardt, Stefan
Falkai, Peter
Antonucci, Linda A.
Bertolino, Alessandro
Liddle, Peter
Koutsouleris, Nikolaos

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 19 June 2024.

License: Creative Commons Attribution Non-commercial No Derivatives


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