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Abstract #4434

Robust motion regression of resting-state data using a convolutional neural network model

Zhengshi Yang1, Xiaowei Zhuang1, Karthik Sreenivasan1, Virendra Mishra1, and Dietmar Cordes1,2

1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2University of Colorado, Boulder, CO, United States

The fluctuation introduced by head motion considerably confounds the interpretation of resting-state fMRI data. Specifying motion regressors without taking fMRI data itself into consideration may not be sufficient to model the impact of head motion. We proposed a robust and automated deep neural network (DNN) to derive motion regressors with both fMRI data and estimated realignment parameters considered. The results show that DNN-derived regressors outperform traditional regressors based on several quality control measurements.

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