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.
Estimating causal brain networks from fMRI data is important in understanding functional human brain connectivity, and current causality estimation methods face various challenges such as high dimensionality and expensive computation. The joint estimation of causal networks between groups shows promising potential to investigate group-related brain connectivity variations. In this paper, we proposed a joint causal brain network estimation method by adding a prior to the popular PC algorithm1 (by Peter Spirtes and Clark Glymour). The prior is obtained through a fast joint Bayesian analysis (FIBA) and plays a role as a screening step, significantly reducing computational burden of PC algorithm. Moreover, the FIBA also enables us to efficiently address the high dimensionality problem of fMRI data. The experimental results from both simulation data sets and real fMRI data demonstrate the accuracy and efficiency of the proposed method. The specific brain connections identified in schizophrenia patients extend previous research and shed light on other studies of mental disorders.
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