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• W4309572144 endingPage "101178" @default.
• W4309572144 startingPage "101178" @default.
• W4309572144 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." @default.
• W4309572144 created "2022-11-28" @default.
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• W4309572144 date "2022-12-01" @default.
• W4309572144 modified "2023-10-14" @default.
• W4309572144 title "Can this data be saved? Techniques for high motion in resting state scans of first grade children" @default.
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• W4309572144 doi "https://doi.org/10.1016/j.dcn.2022.101178" @default.
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