Yezhi Pan, L. Elliot Hong, Ashley Acheson, Paul M. Thompson, Neda Jahanshad, Alyssa H. Zhu, Jiaao Yu, Chixiang Chen, Tianzhou Ma, Ho-Ling Liu, Jelle Veraart, Els Fieremans, Nicole R. Karcher, Peter Kochunov, Shuo Chen
{"title":"ABCD 扩散分数各向异性和皮质厚度的部位可靠性分析:扫描仪平台的影响","authors":"Yezhi Pan, L. Elliot Hong, Ashley Acheson, Paul M. Thompson, Neda Jahanshad, Alyssa H. Zhu, Jiaao Yu, Chixiang Chen, Tianzhou Ma, Ho-Ling Liu, Jelle Veraart, Els Fieremans, Nicole R. Karcher, Peter Kochunov, Shuo Chen","doi":"10.1002/hbm.70070","DOIUrl":null,"url":null,"abstract":"<p>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 nonbiological 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 with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, <i>p</i> < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, <i>p</i> < 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.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 16","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11551787/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Site-Wise Reliability Analysis of the ABCD Diffusion Fractional Anisotropy and Cortical Thickness: Impact of Scanner Platforms\",\"authors\":\"Yezhi Pan, L. Elliot Hong, Ashley Acheson, Paul M. Thompson, Neda Jahanshad, Alyssa H. Zhu, Jiaao Yu, Chixiang Chen, Tianzhou Ma, Ho-Ling Liu, Jelle Veraart, Els Fieremans, Nicole R. Karcher, Peter Kochunov, Shuo Chen\",\"doi\":\"10.1002/hbm.70070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 nonbiological 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 with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, <i>p</i> < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, <i>p</i> < 0.001). 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A Site-Wise Reliability Analysis of the ABCD Diffusion Fractional Anisotropy and Cortical Thickness: Impact of Scanner Platforms
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 nonbiological 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 with 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.
期刊介绍:
Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged.
Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.