TY - JOUR
T1 - Structural and functional brain patterns predict formal thought disorder’s severity and its persistence in recent-onset psychosis
T2 - Results from the PRONIA Study
AU - Buciuman, Madalina-Octavia
AU - Oeztuerk, Oemer Faruk
AU - Popovic, David
AU - Enrico, Paolo
AU - Ruef, Anne
AU - Bieler, Nadia
AU - Sarisik, Elif
AU - Weiske, Johanna
AU - Dong, Mark Sen
AU - Dwyer, Dominic B.
AU - Kambeitz-Ilankovic, Lana
AU - Haas, Shalaila S.
AU - Stainton, Alexandra
AU - Ruhrmann, Stephan
AU - Chisholm, Katharine
AU - Kambeitz, Joseph
AU - Riecher-Rössler, Anita
AU - Upthegrove, Rachel
AU - Schultze-Lutter, Frauke
AU - Salokangas, Raimo K.R.
AU - Hietala, Jarmo
AU - Pantelis, Christos
AU - Lencer, Rebekka
AU - Meisenzahl, Eva
AU - Wood, Stephen J.
AU - Brambilla, Paolo
AU - Borgwardt, Stefan
AU - Falkai, Peter
AU - Antonucci, Linda A.
AU - Bertolino, Alessandro
AU - Liddle, Peter
AU - Koutsouleris, Nikolaos
N1 - 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/].
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Early recognition
KW - Formal thought disorder
KW - Neuroimaging
KW - Predictive modeling
KW - Recent-onset psychosis
KW - Subtyping
UR - https://www.sciencedirect.com/science/article/abs/pii/S2451902223001441
UR - http://www.scopus.com/inward/record.url?scp=85171130078&partnerID=8YFLogxK
U2 - 10.1016/j.bpsc.2023.06.001
DO - 10.1016/j.bpsc.2023.06.001
M3 - Article
SN - 2451-9022
VL - 8
SP - 1207
EP - 1217
JO - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
JF - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
IS - 12
ER -