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Previous studies have demonstrated that the brain functional connectivity undergoes striking temporal dynamics. Modeling dynamically the brain functional connectivity has become not only an urgent and important work, but also a new direction for exploring brain functional research. For this motivation, a novel method for exploring functional brain dynamics based on Fisher linear discriminant (FLD)...
Recently, it has been an increasing interest in modeling abnormal temporal dynamics of functional interactions in psychiatric disorders. However, the accuracy of differentiating attention-deficit/hyperactivity disorder (ADHD) children form normal children has still much space for improvement. To further improve the accuracy, the key issue is to extract more effective features from original fMRI data...
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...
In the human brain mapping field, virtually most existing fMRI activation detection methods, such as the general linear model (GLM), have assumed that the fMRI signal magnitude should follow the alternations of baseline and task periods. However, our extensive observation shows that different brain regions or networks exhibit quite dissimilar temporal activation patterns. Inspired by this observation,...
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...
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