Brain motion networks predict head motion during rest- and task-fMRI.

Abstract

The capacity to stay still during scanning, which is necessary to avoid motion confounds while imaging, varies markedly between people. Here we investigated the effect of head motion on functional connectivity using connectome-based predictive modeling (CPM) and publicly available brain functional magnetic resonance imaging (fMRI) data from 414 individuals with low frame-to-frame motion (Δd < 0.18 mm). Leave-one-out was used for internal cross-validation of head motion prediction in 207 participants, and twofold cross-validation was used in an independent sample (n = 207). Parametric testing, as well as CPM-based permutations for null hypothesis testing, revealed strong linear associations between observed and predicted values of head motion. Motion prediction accuracy was higher for task- than for rest-fMRI, and for absolute head motion (d) than for Δd. Denoising attenuated the predictability of head motion, but stricter framewise displacement threshold (FD = 0.2 mm) for motion censoring did not alter the accuracy of the predictions obtained with lenient censoring (FD = 0.5 mm). For rest-fMRI, prediction accuracy was lower for individuals with low motion (mean Δd < 0.02 mm; n = 200) than for those with moderate motion (Δd < 0.04 mm; n = 414). The cerebellum and default-mode network (DMN) regions that forecasted individual differences in d and Δd during six different tasks- and two rest-fMRI sessions were consistently prone to the deleterious effect of head motion. However, these findings generalized to a novel group of 1,422 individuals but not to simulated datasets without neurobiological contributions, suggesting that cerebellar and DMN connectivity could partially reflect functional signals pertaining to inhibitory motor control during fMRI.