Can this data be saved? Techniques for high motion in resting state scans of first grade children

Jolinda Smith, Eric Wilkey, Ben Clarke, Lina Shanley, Virany Men, Damien Fair, Fred W. Sabb

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Motion remains a significant technical hurdle in fMRI studies of young children. Our aim was to develop a straightforward and effective method for obtaining and preprocessing resting state data from a high-motion pediatric cohort. This approach combines real-time monitoring of head motion with a preprocessing pipeline that uses volume censoring and concatenation alongside independent component analysis based denoising. We evaluated this method using a sample of 108 first grade children (age 6–8) enrolled in a longitudinal study of math development. Data quality was assessed by analyzing the correlation between participant head motion and two key metrics for resting state data, temporal signal-to-noise and functional connectivity. These correlations should be minimal in the absence of noise-related artifacts. We compared these data quality indicators using several censoring thresholds to determine the necessary degree of censoring. Volume censoring was highly effective at removing motion-corrupted volumes and ICA denoising removed much of the remaining motion artifact. With the censoring threshold set to exclude volumes that exceeded a framewise displacement of 0.3 mm, preprocessed data met rigorous standards for data quality while retaining a large majority of subjects (83 % of participants). Overall, results show it is possible to obtain usable resting-state data despite extreme motion in a group of young, untrained subjects.

Original languageEnglish (US)
Article number101178
JournalDevelopmental Cognitive Neuroscience
Volume58
DOIs
StatePublished - Dec 2022
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported by the National Science Foundation ( NSF ) Grants DRL 1660840 and DRL 1748954 to Drs. Ben Clarke, Hank Fien, Fred Sabb, and Lina Shanley and by the U.S. Department of Education (USDE) Institute for Education Sciences ( IES ) Grant R324A160046 to Drs. Ben Clarke, Christian Doabler, and Hank Fiend at the University of Oregon. Eric D. Wilkey is the recipient of a Banting Postdoctoral Fellowship ( NSERC ) and BrainsCAN Postdoctoral Fellowship at Western University, funded by the Canada First Research Excellence Fund ( CFREF ).

Publisher Copyright:
© 2022 The Authors

Keywords

  • Artifact
  • Fmri
  • Independent component analysis
  • Motion
  • Resting-state

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