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2 March 2020 Causal brain network in schizophrenia by a two-step Bayesian network analysis
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Abstract
Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been widely acknowledged that SZ is related to disrupted brain connectivity; however, the underlying neuromechanism has not been fully understood. In the current literature, various methods have been proposed to estimate the association networks of the brain using functional Magnetic Resonance Imaging (fMRI). Approaches that characterize statistical associations are likely a good starting point for estimating brain network interactions. With in-depth research, it is natural to shift to causal interactions. Therefore, we use the fMRI image from the Mind Clinical Imaging Consortium (MCIC) to study the causal brain network of SZ patients. Existing methods have focused on estimating a single directed graphical model but ignored the similarities from related classes. We, thus, design a two-step Bayesian network analysis for this case-control study, which we assume their brain networks are distinct but related. We reveal that compared to healthy people, SZ patients have a diminished ability to combine specialized information from distributed brain regions. Particularly, we have identified 6 hub brain regions in the aberrant connectivity network, which are at the frontal-parietal lobe (Supplementary motor area, Middle frontal gyrus, Inferior parietal gyrus), insula and putamen of the left hemisphere.
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Aiying Zhang, Gemeng Zhang, Vince D. Calhoun, and Yu-Ping Wang "Causal brain network in schizophrenia by a two-step Bayesian network analysis", Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131817 (2 March 2020); https://doi.org/10.1117/12.2549306
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