用多元潜在方法消除扫描仪效应:ABCD成像数据的RELIEF?

Dominik Kraft, G. M. Bon, Édith Breton, Philipp Seidel, Tobias Kaufmann
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摘要

摘要 当数据被汇集到不同的研究地点时,扫描地点协调是任何神经影像分析的关键部分。Zhang 及其同事最近推出了多变量协调方法 RELIEF(通过因式分解消除潜在的扫描器间效应),旨在消除显性和潜在的扫描部位效应。该方法在成人样本中进行了初步验证,结果显示其性能优于现有方法。在此,我们试图研究 RELIEF 在协调青少年大脑和认知发展(ABCD)研究数据方面的实用性,该研究是一项广泛使用的脑发育成像资源。我们将 RELIEF 与未协调数据、ComBat 和 CovBat 协调数据进行了比对,并研究了制造商类型、样本大小和样本年龄范围较窄对协调性能的影响。我们发现,在对样本量足够大的研究机构进行协调的情况下,RELIEF 的性能优于其他技术,但在纳入样本量非常小的研究机构的情况下,RELIEF 的性能存在很大的差异。因此,我们的研究结果突出表明,在协调样本不平衡的数据集(如 ABCD 队列)时,需要进行仔细的质量控制。我们的评论和共享脚本可为其他希望在 ABCD 相关工作中整合最佳实践的学者提供指导。
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Removing scanner effects with a multivariate latent approach: A RELIEF for the ABCD imaging data?
Abstract Scan site harmonization is a crucial part of any neuroimaging analysis when data have been pooled across different study sites. Zhang and colleagues recently introduced the multivariate harmonization method RELIEF (REmoval of Latent Inter-scanner Effects through Factorization), aiming to remove explicit and latent scan site effects. Their initial validation in an adult sample showed superior performance compared to established methods. We here sought to investigate utility of RELIEF in harmonizing data from the Adolescent Brain and Cognitive Development (ABCD) study, a widely used resource for developmental brain imaging. We benchmarked RELIEF against unharmonized, ComBat, and CovBat harmonized data and investigated the impact of manufacturer type, sample size, and a narrow sample age range on harmonization performance. We found that in cases where sites with sufficiently large samples were harmonized, RELIEF outperformed other techniques, yet in cases where sites with very small samples were included there was substantial performance variation unique to RELIEF. Our results therefore highlight the need for careful quality control when harmonizing data sets with imbalanced samples like the ABCD cohort. Our comment alongside shared scripts may provide guidance for other scholars wanting to integrate best practices in their ABCD related work.
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