1. Can this data be saved? Techniques for high motion in resting state scans of first grade children
- Author
-
Jolinda Smith, Eric Wilkey, Ben Clarke, Lina Shanley, Virany Men, Damien Fair, and Fred W. Sabb
- Subjects
Fmri ,Motion ,Artifact ,Resting-state ,Independent component analysis ,Neurophysiology and neuropsychology ,QP351-495 - 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.
- Published
- 2022
- Full Text
- View/download PDF