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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