An open discussion in functional connectivity (FC) studies is the mitigation of motion-related artifacts. Data-driven denoising such as Independent Component Analysis (ICA) could help in improving the reproducibility of results, however, the definition of pipelines to deal with mild to high motion cases is still controversial. We estimate the effect of different workflows optimized for best-controlling head motion both in healthy and Parkinson’s Disease cohorts. Regardless of baseline head motion level, ICA-based control of motion confounds affects functional connectivity metrics, with non-negligible impact on static connectivity and most severe effects on temporal and spatial features of dynamic functional connectivity measures.
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