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Dynamic functional interaction has received much attention recently in the field of neuroimaging. Past studies reveal that the dynamics of functional interactions only exists in part of brain. In this paper, a novel Bayesian inference model is developed to bi-partition the brain regions into dynamic/stable sub networks and to simultaneously segment the temporal sequence of dynamic network into several...
Multiple recent neuroimaging studies have demonstrated that the human brain's function undergoes remarkable temporal dynamics. However, quantitative characterization and modeling of such functional dynamics have been rarely explored. To fill this gap, we presents a novel Bayesian connectivity change point model (BCCPM), to analyze the joint probabilities among the nodes of brain networks between different...
Based on the structural connectomes constructed from diffusion tensor imaging (DTI) data, we present a novel framework to discover functional connectomics signatures from resting-state fMRI (R-fMRI) data for the characterization of brain conditions. First, by applying a sliding time window approach, the brain states represented by functional connectomes were automatically divided into temporal quasi-stable...
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