剖析扩散MRI标准化方法ComBAT的局限，提出五大实操建议。
ComBAT Harmonization for diffusion MRI: Challenges and Best Practices
- 分析ComBAT方法的数学假设及其失效风险。
- 实验验证人口规模、年龄分布等对结果的影响。
- 给出提升可复现性与协作研究的实用指南。
多年来，ComBAT已成为扩散MRI测量值标准化的标准方法，能够补偿站点相关的加性与乘性偏差，同时保留生物学变异。然而，ComBAT依赖一系列假设，若这些假设被违反，可能导致标准化结果失真。本文系统回顾ComBAT的数学基础，明确其假设条件，并探讨其对理想人群构成的要求。通过一系列实验，采用专为规范建模设计的改进版Pairwise-ComBAT，评估了人群规模、年龄分布、特定协变量缺失以及加性与乘性因子大小等因素的影响。基于实验结果，提出五项关键建议，以增强结果一致性与可复现性，助力开放科学、协作研究及临床实际应用。
原文摘要 · Abstract (English)
Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT's mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results. Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。