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Abstract #2351

Implementation of automated resting-state presurgical fMRI analysis pipelines with patient-based probabilistic functional atlases

Kevin D Tran1, Jian Ming Teo2,3, Vinodh A Kumar4, Mu-Lan Jen2, Cathy Elsinger1, and Ho-Ling Liu2
1NordicNeuroLab Inc., Milwaukee, WI, United States, 2Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 3The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States, 4Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: Functional Connectivity, fMRI (resting state)

Motivation: To develop clinical software for automated localization of critical functional networks with presurgical resting-state (rs) functional MRI (fMRI).

Goal(s): To develop analysis pipelines integrating patient-based probabilistic functional atlases and evaluate their effectiveness for mapping motor (hand, tongue, foot), visual, and language networks.

Approach: The software implemented motor, visual, and language atlases, created using presurgical tb-fMRI of brain tumor patients, for seed-based correlation and independent component analysis on rs-fMRI patient data. The results were compared to tb-fMRI.

Results: The distances between the resulting functional connectivity peaks and tb-fMRI activation peaks were equivalent to those obtained by seeding based on tb-fMRI activation.

Impact: This work developed software that automates rs-fMRI analysis by implementing probabilistic functional atlases in SBC and ICA pipelines. It supports routine clinical use of rs-fMRI for presurgical mapping of motor, visual, and language areas for patients undergoing brain tumor resection.

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