An alternative approach towards assessing and accounting for individual motion in fMRI timeseries.

Motion is a significant problem for the analysis of functional MRI data. This manuscript addresses the question of whether an individualized assessment of motion may be informative, and whether it may be beneficial with regard to explaining motion-related variance. Two independent datasets are used to explore and test this hypothesis, from a total of 21 healthy children, performing either no externally-cued task (resting state) or an active listening paradigm (beep story). Translations and rotations are combined into one single, individual measure of total displacement, which is demonstrated to be substantially different between brain regions as a function of their distance from the individual origin. An increasing number of covariates leads to a loss of detection power, but more so on the first than on the second level, and more so in less-powerful designs. Synthetic timeseries are calculated from which the direct effects of motion as well as motion*B0 effects can be isolated, allowing to extract individual timecourses which reflect both direct and indirect motion effects. Including three timecourses from such an individually-derived "motion fingerprint" into first-level statistical analyses explains variance to a similar degree as the commonly-used approach of including the realignment parameters, and performance is statistically equivalent to including the realignment parameters on the second level. A more individualized approach to explaining motion-related variance may therefore be beneficial, depending on the scenario.