Meeting Banner
Abstract #3774

Automatic EEG-assisted retrospective fMRI head motions correction improves rs-fMRI connectivity analysis

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here