用ComBat-GAM方法消除婴儿脑部MRI扫描仪差异，提升数据可比性。
Harmonization mitigates diffusion MRI scanner effects in infancy: insights from the HEALthy Brain and Childhood Development (HBCD) study
- 采用ComBat-GAM算法校正六种扫描仪带来的数据偏差。
- 校正后各扫描仪间指标差异不再显著，效应量大幅降低。
- 适合做大规模婴幼儿脑发育研究的学者参考使用。
HEALthy Brain and Childhood Development（HBCD）研究是一项旨在理解群体水平脑发育的纵向项目，但大规模研究需克服站点相关变异并保留生物学信号。除了扩散加权磁共振成像数据外，HBCD数据集还提供可直接分析的衍生数据，包括预设脑白质束中的弥散张量成像（DTI）指标。本研究首次系统评估了HBCD数据中扫描仪型号相关的变异影响，并在当前数据发布版本1.1中，针对六种扫描仪模型应用ComBat-GAM进行谐波化处理。经校正后，所有指标在多重检验校正（FDR）下均无显著差异，且所有度量的Cohen's f效应量均下降。研究强调了大规模研究中严格数据谐波化的重要性，建议未来对HBCD数据的研究应控制此类效应。
原文摘要 · Abstract (English)
The HEALthy Brain and Childhood Development (HBCD) Study is an ongoing longitudinal initiative to understand population-level brain maturation; however, large-scale studies must overcome site-related variance and preserve biologically relevant signal. In addition to diffusion-weighted magnetic resonance imaging images, the HBCD dataset offers analysis-ready derivatives for scientists to conduct their analysis, including scalar diffusion tensor (DTI) metrics in a predetermined set of bundles. The purpose of this study is to characterize HBCD-specific site effects in diffusion MRI data, which have not been systematically reported. In this work, we investigate the sensitivity of HBCD bundle metrics to scanner model-related variance and address these variations with ComBat-GAM harmonization within the current HBCD data release 1.1 across six scanner models. Following ComBat-GAM, we observe zero statistically significant differences between the distributions from any scanner model following FDR correction and reduce Cohen's f effect sizes across all metrics. Our work underscores the importance of rigorous harmonization efforts in large-scale studies, and we encourage future investigations of HBCD data to control for these effects.
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