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Abstract #0357

Denoising task-correlated head motion in motor-task fMRI data using multi-echo ICA

Neha A. Reddy1,2, Rachael C. Stickland1, Kimberly J. Hemmerling1,2, Kristina M. Zvolanek1,2, César Caballero-Gaudes3, and Molly G. Bright1,2
1Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, United States, 2Biomedical Engineering, Northwestern University, Evanston, IL, United States, 3Basque Center on Cognition, Brain and Language, Donostia, Spain


Multi-echo independent component analysis (ME-ICA) has been shown to differentiate the effects of head motion from desired BOLD signal in fMRI data, but this method has not been tested in motor-task studies with high amounts of task-correlated head motion. We investigated four denoising models on multi-echo motor-task data with limited and amplified task-correlated motion: Aggressive, Moderate, and Conservative ME-ICA nuisance regression models and a conventional optimally combined (OC) model. ME-ICA models were found to better dissociate head motion and BOLD signal variance than the OC model. Among them, the Aggressive model had the most consistent activation results with amplified motion.

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