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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