A1 Refereed original research article in a scientific journal
Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study
Authors: Buciuman Madalina O, 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, Kambeitz Joseph, Riecher-Rössler Anita, Upthegrove Rachel, Schultze-Lutter Frauke, Salokangas Raimo KR, 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; PRONIA Consortium
Publisher: Elsevier
Publication year: 2023
Journal: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Journal name in source: Biological psychiatry. Cognitive neuroscience and neuroimaging
Journal acronym: Biol Psychiatry Cogn Neurosci Neuroimaging
ISSN: 2451-9022
eISSN: 2451-9030
DOI: https://doi.org/10.1016/j.bpsc.2023.06.001
Web address : https://doi.org/10.1016/j.bpsc.2023.06.001
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.
