ArticlebioRxiv : the preprint server for biology2024
A site-wise reliability analysis of the ABCD diffusion fractional anisotropy and cortical thickness: impact of scanner platforms.
Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce non-biological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61±0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76±0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared to those using GE/Philips scanners for both FA (AICC=0.71±0.12 vs 0.46±0.17, p<0.001) and CT (AICC=0.80±0.10 vs 0.69±0.11, p<0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.
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