The impact of different motion censoring thresholds on functional connectivity in the ABCD dataset
Poster No:
1596
Submission Type:
Late-Breaking Abstract Submission
Authors:
Rasmus Birn1, Matthew Peverill1, Taylor Keding2, Justin Russell1, Ryan Herringa1
Institutions:
1University of Wisconsin - Madison, Madison, WI, 2Yale University, New Haven, CT
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Introduction:
Head motion continues to be a significant source of noise in resting-state functional connectivity MRI (rs-fcMRI). Strategies to reduce the impact of motion include image realignment, nuisance regression, and censoring high-motion time points. A common motion censoring threshold in rs-fcMRI is to exclude time points whose framewise displacement (volume-to-volume motion) exceeds 0.2mm. A recent study by Birn (2023) showed that this threshold can sometimes result in poorer estimates of functional connectivity in datasets with shorter scan durations (e.g. 5 minutes of data with 2s TR) due to the low degrees of freedom remaining after motion censoring and nuisance regression. It is unclear if a similar result would hold in acquisitions with multiple runs and shorter TR (800ms), such as those used in the Adolescent Brain and Cognitive Development (ABCD) study. We therefore examined the impact of different motion censoring thresholds on rs-fcMRI estimates from the ABCD study. We also examined the impact of bandpass filtering.
Methods:
Resting-state fMRI data were downloaded from 1004 subjects from the baseline release of ABCD (Casey et al., 2018). Each participant had between 1-4 resting-state runs (TR: 800ms, 375 volumes). Data were preprocessed as in Birn (2023). Bandpass filtering was performed by including a set of sines and cosines in the nuisance regression. Whole-brain functional connectivity (FC) matrices were computed by averaging the preprocessed time series, concatenated across all runs, over 333 ROIs (Gordon, et al., 2016) and computing all pairwise correlations.
Volume-to-volume motion was estimated as the Euclidean norm (Enorm) of the temporal difference of the 6 realignment parameters, after filtering out frequencies related to respiration (Fair et al., 2020). Six different motion censoring thresholds were evaluated: 0.2mm, 0.3mm, 0.4mm, 0.5mm, 1.0mm, 5.0mm.
The impact of motion on FC was assessed using two metrics: 1) the correlation between the FC and the mean Enorm (QC-FC) (Ciric, et al., 2018), and the spatial similarity between each subject's functional connectivity matrix and the group average FC matrix (Birn 2023).
Volume-to-volume motion was estimated as the Euclidean norm (Enorm) of the temporal difference of the 6 realignment parameters, after filtering out frequencies related to respiration (Fair et al., 2020). Six different motion censoring thresholds were evaluated: 0.2mm, 0.3mm, 0.4mm, 0.5mm, 1.0mm, 5.0mm.
The impact of motion on FC was assessed using two metrics: 1) the correlation between the FC and the mean Enorm (QC-FC) (Ciric, et al., 2018), and the spatial similarity between each subject's functional connectivity matrix and the group average FC matrix (Birn 2023).
Results:
Out of the 1004 subjects with resting-state data, 796 subjects had sufficient time points left after censoring and nuisance regression to estimate FC. QC-FC shows the smallest values for the most stringent (0.2mm) motion censoring, and greater values for higher motion censoring thresholds (Fig 1). The similarity of each subject's FC matrix to the group average decreased with increasing motion (across subjects) (Fig 2). Interestingly, high-motion subjects showed increased similarity to the group-mean FC matrix as the motion censoring threshold was increased from 0.2mm to 0.3mm and 0.4mm (Fig 2). This is opposite to what one would expect -- if motion is decreasing the similarity to the group mean, then including more high-motion time points should decrease the similarity. Closer examination suggests that this increase in similarity is due to an increase in the degrees of freedom. A large number of degrees of freedom are lost due to the bandpass filtering. When this analysis was repeated without bandpass filtering, the similarity no longer varies with the mean amount of motion.
Conclusions:
More stringent motion censoring does reduce the association between functional connectivity and motion, as measured by the QC-FC metric, but it also reduces the degrees of freedom, which can negatively impact the functional connectivity estimate. Investigators should therefore be aware of the degrees of freedom left after the motion censoring and nuisance regression. Future studies may also want to re-evaluate the use of bandpass filtering particularly in studies that acquire limited amount of data.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Motion Correction and Preprocessing 1
Task-Independent and Resting-State Analysis
Keywords:
Data analysis
Development
FUNCTIONAL MRI
Open Data
PEDIATRIC
Statistical Methods
1|2Indicates the priority used for review
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Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., Orr, C. A., Wager, T. D., Banich, M. T., Speer, N. K., Sutherland, M. T., Riedel, M. C., Dick, A. S., Bjork, J. M., Thomas, K. M.,…Workgroup, A. I. A. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci, 32, 43-54. https://doi.org/10.1016/j.dcn.2018.03.001
Ciric, R., Rosen, A. F. G., Erus, G., Cieslak, M., Adebimpe, A., Cook, P. A., Bassett, D. S., Davatzikos, C., Wolf, D. H., & Satterthwaite, T. D. (2018). Mitigating head motion artifact in functional connectivity MRI. Nat Protoc, 13(12), 2801-2826. https://doi.org/10.1038/s41596-018-0065-y
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