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- W2938876031 abstract "Functional connectivity (FC) has proven to be a promising approach for understanding alterations in interregional associations that are linked to disease processes such as major depressive disorder (MDD). There is emerging evidence toward dynamic patterns of FC, although the vast majority of studies consider only static scalar-valued summaries of FC between pairs of brain regions. Many current approaches to dynamic FC use sliding windows, which are widely known to be highly sensitive to the choice of window length. We are interested in whether there are distinct change-points in the evolving network structure. We develop a method that leverages the entire covariance matrix, enabling relatively fast and efficient change-point estimation. Brain networks can be explored with the suggested covariance structure with change analysis. Our model incorporates multivariate time series imaging data, reflecting spatial correlations between distinct brain regions. We evaluate these methods with simulated data and resting state fMRI data." @default.
- W2938876031 created "2019-04-25" @default.
- W2938876031 creator A5040867140 @default.
- W2938876031 date "2019-01-01" @default.
- W2938876031 modified "2023-09-29" @default.
- W2938876031 title "Change-Point Estimation and Testing for Brain Functional Connectivity Networks" @default.
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- W2938876031 doi "https://doi.org/10.1109/kst.2019.8687756" @default.
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