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The estimation of time varying networks for functional magnetic resonance imaging data sets is of increasing importance and interest. We formulate the problem in a high dimensional time series framework and introduce a data‐driven method, namely network change points detection, which detects change points in the network structure of a multivariate time series, with each component of the time series...
Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta‐analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field...
Statistical modelling of functional magnetic resonance imaging data is challenging as the data are both spatially and temporally correlated. Spatially, measurements are taken at thousands of contiguous regions, called voxels, and temporally measurements are taken at hundreds of time points at each voxel. Recent advances in Bayesian hierarchical modelling have addressed the challenges of spatiotemporal...
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