Does motion-related brain functional connectivity reflect both artifacts and genuine neural activity?

Abstract

Imaging research on functional connectivity is uniquely contributing to characterize the functional organization of the human brain. Functional connectivity measurements, however, may be significantly influenced by head motion that occurs during image acquisition. The identification of how motion influences such measurements is therefore highly relevant to the interpretation of a study's results. We have mapped the effect of head motion on functional connectivity in six different populations representing a wide range of potential influences of motion on functional connectivity. Group-level voxel-wise maps of the correlation between a summary head motion measurement and functional connectivity degree were estimated in 80 young adults, 71 children, 53 older adults, 20 patients with Down syndrome, 24 with Prader-Willi syndrome and 20 with Williams syndrome. In highly compliant young adults, motion correlated with functional connectivity measurements showing a system-specific anatomy involving the sensorimotor cortex, visual areas and default mode network. Further characterization was strongly indicative of these changes expressing genuine neural activity related to motion, as opposed to pure motion artifact. In the populations with larger head motion, results were more indicative of widespread artifacts, but showing notably distinct spatial distribution patterns. Group-level regression of motion effects was efficient in removing both generalized changes and changes putatively related to neural activity. Overall, this study endorses a relatively simple approach for mapping distinct effects of head motion on functional connectivity. Importantly, our findings support the intriguing hypothesis that a component of motion-related changes may reflect system-specific neural activity.

Anomalous basal ganglia connectivity and obsessive–compulsive behaviour in patients with Prader Willi syndrome

“…Participants with large head motion (boxplot -defined outliers) 30 were excluded. Time series were aligned to the first image volume in each participant using a least squares minimization and a 6-parameter (rigid body) spatial transformation.…”

“…Within-subject, censoring-based MRI signal artifact removal 31 (scrubbing) was used to discard motion-affected volumes. For each participant, interframe motion measurements 30 served as an index of data quality to flag volumes of suspect quality across the run. At points with interframe motion > 0.2 mm, we discarded that corresponding volume, the immediately preceding and the succeeding 2 volumes.…”

“…Finally, potential motion effects were removed using a summary measurement for each participant (mean interframe motion across the fMRI run) as a regressor in the second-level (group) analyses in SPM. 30 …”

“…Another potential limitation in assessing patients with a degree of intellectual disability is the risk of excessive head motion artifact on brain functional connectivity measurements. 30 To mitigate this concern, as detailed in the Methods section, we adopted a variety of procedures that have been previously reported to be effective in removing motion effects in such populations. Also, 6-min resting-state fMRI may be considered a relatively short assessment.…”

Facial emotion processing in patients with social anxiety disorder and Williams–Beuren syndrome: an fMRI study

“…Mean volume-to volume translation was used as a regressor in the group (second-level) analyses as a procedure to remove motion effects as previously described. 30 First-level (single-subject) SPM contrast images were estimated for the following task effects of interest: all faces > shapes, fearful faces > shapes, angry faces > shapes and happy faces > shapes. For each participant, different task regressors were created for each task condition (cross fixation, shapes, angry, fear and happy) by specifying the onset and duration of each task block (multiple regressors option).…”