Correcting Motion Induced Connectivity Changes in Resting-State fMRI

Ali-mohammad Golestani¹, Mariana Lazar¹

Head motion during Resting-State fMRI artificially alters functional connectivity maps, with its effects persisting even after typical correction including frame realignment and regression of motion parameters. Adequate methods for correcting motion artifacts are currently a topic of debate. In this study we compared the ability of basic correction, data scrubbing (excluding volumes with excessive motion from the dataset), and independent component analysis (identifying and excluding motion components with ICA) to correct motion-induced connectivity alterations. Our result shows that ICA outperforms basic correction and data scrubbing and can suppress motion-induced connectivity changes.