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Abstract #2476

Static and dynamic connectivity are differentially affected by head motion: a healthy control and Parkinson’s disease study

Francesca Saviola1, Stefano Tambalo1, Donna Gift Cabalo1, Lisa Novello1, Enrica Pierotti1, Alessandra Dodich1, Costanza Papagno1, Luca Turella1, Dimitri Van De Ville2,3, and Jorge Jovicich1
1University of Trento, Center for Mind/Brain Sciences, Rovereto, Italy, 2Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 3Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland


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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