Structural and Functional Brain Patterns Predict Formal Thought Disorder’s Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

神经影像学 白质 精神病 显著性(神经科学) 心理学 持久性(不连续性) 多元统计 灰质 大脑大小 灰色(单位) 听力学 神经科学 临床心理学 精神科 医学 磁共振成像 机器学习 计算机科学 核医学 岩土工程 工程类 放射科
作者
M.O. Buciuman,Oemer Faruk Oeztuerk,David Popovic,Paolo Enrico,Anne Ruef,Nadia Bieler,Elif Sarisik,Johanna Weiske,Mark Sen Dong,Dominic 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. Salokangas,Jarmo Hietala,Christos Pantelis,Rebekka Lencer,Eva Meisenzahl,Stephen Wood,Paolo Brambilla,Stefan Borgwardt,Peter Falkai,Linda A. Antonucci,Alessandro Bertolino,Peter F. Liddle,Nikolaos Koutsouleris
出处
期刊:Biological Psychiatry: Cognitive Neuroscience and Neuroimaging [Elsevier BV]
卷期号:8 (12): 1207-1217
标识
DOI:10.1016/j.bpsc.2023.06.001
摘要

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. 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. 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%. 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.
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