期刊
SCHIZOPHRENIA BULLETIN
卷 49, 期 1, 页码 172-184出版社
OXFORD UNIV PRESS
DOI: 10.1093/schbul/sbac158
关键词
psychosis disorders; multimodal fusion; diagnosis; clinical phenotypes; brain network
类别
Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder have significant overlap in clinical features, brain abnormalities, and genetic risk factors. This study aims to identify multimodal brain networks associated with psychotic symptom, mood, and cognition to differentiate among these disorders. The findings suggest shared brain networks implicated in prefrontal, medial temporal, anterior cingulate, and insular cortices, although they are linked to different clinical domains. The identified networks have the potential to serve as biomarkers for distinguishing among these disorders and understanding their underlying mechanisms.
Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective.
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