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Can this data be saved? Techniques for high motion in resting state scans of first grade children
Jolinda Smith1, Eric Wilkey2, Ben Clarke3
1Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, OR, USA.
Insights
This study presents a new method for fMRI data preprocessing in young children, effectively reducing motion artifacts. The approach ensures high-quality resting-state data even in high-motion pediatric cohorts.
Area of Science:
- Neuroscience
- Medical Imaging
- Pediatric Research
Background:
- Head motion is a major challenge in pediatric functional MRI (fMRI).
- Acquiring high-quality resting-state fMRI data from young children is difficult due to motion.
- Existing methods often struggle with motion artifacts in pediatric neuroimaging.
Purpose of the Study:
- To develop an effective method for obtaining and preprocessing resting-state fMRI data from a high-motion pediatric cohort.
- To address the technical hurdle of motion in fMRI studies of young children.
- To establish rigorous data quality standards for pediatric fMRI.
Main Methods:
- Combined real-time head motion monitoring with a preprocessing pipeline.
- Utilized volume censoring, concatenation, and independent component analysis (ICA) based denoising.
- Evaluated method on 108 first-grade children (ages 6-8) in a longitudinal math development study.
Main Results:
- Volume censoring effectively removed motion-corrupted data.
- ICA denoising significantly reduced remaining motion artifacts.
- A framewise displacement threshold of 0.3 mm for censoring retained 83% of participants with high data quality.
Conclusions:
- The developed method successfully obtains usable resting-state fMRI data from young children with significant motion.
- This approach meets rigorous data quality standards for pediatric neuroimaging.
- It is feasible to acquire valuable fMRI data from challenging pediatric populations.
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.
