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Abstract #4195

Evaluating Effective Connectivity in Auditory-Motor fMRI Using Dynamic Granger Causality Analysis

Yeh-Hsiung Cheng 1 , I-Jung Chen 2 , Tzu-Cheng Chao 1,2 , and Ming-Long Wu 1,2

1 Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan, 2 Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

Studies have shown that human brain activities are dynamic and could vary during fMRI experiment. Here, we propose a windowing-based Granger causality analysis for evaluating effective connectivity (EC) in fMRI data called dynamic Granger causality analysis (DGCA). Principal Granger causality patterns obtained from subjects reflect common brain states among subjects while processing the auditory-motor task. Results show that DGCA provides more EC information that could potentially provide more knowledge of dynamic changes in the brain.

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