Human brain is the most complicated and efficient information processing system. FMRI imaging studies have revealed the presence of low frequency fluctuations in the brain, which indicates the functional connectivity between anatomical separated brain regions and reflects the coherency of underlying neuronal activity of these regions. Regions that show this kind of coherent functional fluctuations are said to form a functional network. Research on brain functional networks will help better understanding of brain function and diagnosis of mental disease. Applying correct and effective methods for analysis of brain functional networks is of great essence and significance. Therefore, methods of brain functional network analysis are an important research content. There are two major kinds of approaches to analyze brain functional networks using functional MRI: hypothesis based method and data driven method. The most common hypothesis based method is region of interests (ROIs) based correlation analysis method, which has been useful and widely used because of its simplicity and ease of interpretation. However, the identification of reliable, reproducible and accurate ROIs for individuals is of great importance due to those uncertain boundaries of functional areas, individual differences across subjects, and vulnerability of functional connectivity to the selection of ROIs. Among data driven methods, ICA has gained popularity for functional network analysis due to its reliability and reproducibility, and its resulting independent components are taken as different functional networks after careful identification. The shortcoming of ICA is its sensitivity to the number of ICs used in computation. Furthermore, how to to preserve independence of ICs at the subject level and establish correspondence of ICs across subjects is the biggest difficulty due to the random order of components. Clustering provides a potentially powerful data-driven approach to functional network extraction, which allows exploring the functional networks though grouping fMRI time series according to their similarities. However, with respect to clustering based methods there is no straightforward means to determine a appropriate number of clusters to identify. The study aims to propose effective and reliable approaches for analyzing brain functional networks, which involve the following parts. (1) Group independent component analysis (ICA) has been widely applied to studies of multi-subje...
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