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• W4306386314 abstract "Dynamic coupling phenomena characterize a widespread fundamental mechanism for the functional brain, which involves large-scale interactions at a multi-level. The Granger causality analysis (GCA) provides a data-driven procedure to investigate causal connections and has the potential to be a powerful dynamic capturing tool. In this paper, distinct from the conventional two-stage scheme of most GCA methods, we suggest a unified GCA (uGCA) method incorporating a sliding window to further capture dynamic connections. And the uGCA method integrates all related procedures into the same space by a single mathematical theory, which involves a description length guided framework. Through synthetic data experiments and real fMRI data experiments, we illustrated the effectiveness and priority of the proposed uGCA method. By varying the data length, we have demonstrated its superiority to conventional GCA in synthetic data experiments. We further illustrated the outstanding capability of their dynamic causal investigation in the fMRI data, involving serial mental arithmetic tasks under visual and auditory stimuli, respectively, one can evaluate the performance of different methods by accessing their network similarities among different stimuli. When varying windows size and step size of the sliding window, respectively, compared with conventional GCA, the uGCA identified higher network similarities while ensuring more robust performance. The stability and effectiveness of uGCA will show it an advantage in the further research of multi-level dynamic coupling and characterizing." @default.
• W4306386314 created "2022-10-17" @default.
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• W4306386314 date "2023-01-01" @default.
• W4306386314 modified "2023-10-18" @default.
• W4306386314 title "Investigating dynamic causal network with unified Granger causality analysis" @default.
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• W4306386314 doi "https://doi.org/10.1016/j.jneumeth.2022.109720" @default.
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