The brain underpinnings of schizophrenia and bipolar disorders are multidimensional, reflecting complex pathological processes and causal pathways, requiring multivariate techniques to disentangle. Furthermore, little is known about the complementary clinical value of brain structural phenotypes when combined with data on cognitive performance and genetic risk. Using data-driven fusion of cortical thickness, surface area, and gray matter density maps (GMD), we found six biologically meaningful patterns showing strong group effects, including four statistically independent multimodal patterns reflecting co-occurring alterations in thickness and GMD in patients, over and above two other independent patterns of widespread thickness and area reduction. Case-control classification using cognitive scores alone revealed high accuracy, and adding imaging features or polygenic risk scores increased performance, suggesting their complementary predictive value with cognitive scores being the most sensitive features. Multivariate pattern analyses reveal distinct patterns of brain morphology in mental disorders, provide insights on the relative importance between brain structure, cognitive and polygenetic risk score in classification of patients, and demonstrate the importance of multivariate approaches in studying the pathophysiological substrate of these complex disorders.
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AUC, Area Under The Receiver-Operating-Characteristic Curve, BD, Bipolar Disorder, Bipolar disorder, Brain structure, Classification, Clinical prediction, Cognition, GLM, General Linear Model, GMD, Gray Matter Density, HC, Controls, LICA, Linked Independent Component Analysis, Multimodal MRI, PGRS, Polygenic Risk Score, Polygenic risk, SZ, Schizophrenia, Schizophrenia, VBM, Voxel Based Morphometry, Adult, Bipolar Disorder, Brain, Female, Humans, Image Interpretation, Computer-Assisted, Machine Learning, Magnetic Resonance Imaging, Male, Multifactorial Inheritance, Multivariate Analysis, Neuroimaging, Schizophrenia