Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

ABSTRACT: Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using

SUBMITTER: Reddy NA 

PROVIDER: S-EPMC10370165 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature