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

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.