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Abstract:
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A prominent assumption in the study of brain functional connectivity is the stationarity of the brain network. However, it is increasingly recognized that the brain network is prone to variations across the scanning session, fueling the need for dynamic connectivity approaches. One of the challenges in developing such approaches is that the frequency and change points for the brain organization are unknown with potentially rapid changes over the scanning session. In order to provide greater power to detect rapidly evolving connectivity changes, we propose a fully automated two-stage approach which pools information across multiple subjects, in order to divide the scanning session into non-overlapping time intervals, such that each interval is characterized by a distinct brain network. The number and positions of the time intervals are unknown and learned from the data in the first stage, by approximating the multivariate time series of correlations using a piecewise constant function. In the second stage, the brain functional network for each time interval is inferred via sparse inverse covariance matrices. Numerical experiments show the advantages of the proposed approach.
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