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 Bodurka^1,2,3

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.