Can this data be saved? Techniques for high motion in resting state scans of first grade children. Smith, J., Wilkey, E., Clarke, B., Shanley, L., Men, V., Fair, D., & Sabb, F. W. Developmental Cognitive Neuroscience, 58:101178, December, 2022. ![Can this data be saved? Techniques for high motion in resting state scans of first grade children [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{smith_can_2022,
title = {Can this data be saved? {Techniques} for high motion in resting state scans of first grade children},
volume = {58},
issn = {1878-9293},
shorttitle = {Can this data be saved?},
url = {https://www.sciencedirect.com/science/article/pii/S1878929322001219},
doi = {10.1016/j.dcn.2022.101178},
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.},
language = {en},
urldate = {2023-08-03},
journal = {Developmental Cognitive Neuroscience},
author = {Smith, Jolinda and Wilkey, Eric and Clarke, Ben and Shanley, Lina and Men, Virany and Fair, Damien and Sabb, Fred W.},
month = dec,
year = {2022},
keywords = {Artifact, Fmri, Independent component analysis, Motion, Resting-state},
pages = {101178},
}
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