Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study
No Preview Available
Citations
Scopus:
Altmetric:
Altmetric::
Associated Records
Authors
File Licence
Editors
Date
2023-12-01
Source Title
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Journal Title
Journal ISSN
Volume Title
Publisher
ELSEVIER
Melbourne Authors
Affiliation
Centre for Youth Mental Health
Psychiatry
Psychiatry
Document Type
Journal Article
Subject Code
Citation
Buciuman, M. O., Oeztuerk, O. F., Popovic, D., Enrico, P., Ruef, A., Bieler, N., Sarisik, E., Weiske, J., Dong, M. S., Dwyer, D. B., Kambeitz-Ilankovic, L., Haas, S. S., Stainton, A., Ruhrmann, S., Chisholm, K., Kambeitz, J., Riecher-Rössler, A., Upthegrove, R., Schultze-Lutter, F. ,... Koutsouleris, N. (2023). Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study. Biological Psychiatry Cognitive Neuroscience and Neuroimaging, 8 (12), pp.1207-1217. https://doi.org/10.1016/j.bpsc.2023.06.001.
Access Status
This item is currently not available from this repository
Access URL
Open Access URL
Open Access at PMC
Linked Resource URL
NHMRC Grant Code
ARC Grant Code
Description
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.