Chung-Ki Wong1, Vadim Zotev1, Masaya Misaki1, Raquel Phillips1, Qingfei Luo1, and Jerzy Bodurka1,2,3
1Laureate Institute for Brain Research, Tulsa, OK, United States, 2College of Engineering, University of Oklahoma, Norman, OK, United States, 3Center for Biomedical Engineering, University of Oklahoma, Norman, OK, United States
We
utilized an automatic EEG-assisted retrospective motion correction (aE-REMCOR)
to improve rs-fMRI connectivity analysis. The aE-REMCOR utilizes EEG data to automatically
correct for head movements in fMRI on a slice-by-slice basis. We compared the
results of seed-based (posterior cingulate cortex) default-mode network (DMN)
connectivity analysis performed with and without aE-REMCOR. The aE-REMCOR
reduced the motion-induced position-dependent error in the DMN connectivity
analysis. The results show the importance of slice-by-slice fMRI motion
corrections to improve rs-fMRI connectivity accuracy especially when the entire
group of subjects exhibits rapid head motions, and also provide incentive for
conducting simultaneous EEG&fMRI.
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