用统计模型量化大脑结构异常，无需对照组也能发现疾病影响。
Normative Modelling in Neuroimaging: A Practical Guide for Researchers
- 基于群体数据建模，估算个体偏离正常水平的程度。
- 可同时调整年龄、性别等变量，识别病理相关脑结构变化。
- 适合临床神经影像研究者，尤其缺乏对照组的场景。
规范建模是神经影像学中日益普遍的统计方法，用于估计脑结构的群体基准值。它能在不依赖大规模匹配对照组的情况下，量化个体与预期分布的偏离，并控制生物与技术协变量。这种方法为识别与病理相关的脑结构改变提供了强大替代方案。尽管已有多种建模方法和预训练模型工具箱可用，但其各自优势与局限性使研究者难以判断何时何地适用。本文通过癫痫临床数据的实例，提供实践指导，阐述统计考量，推动预训练模型在神经影像研究中的负责任、规范化应用。
原文摘要 · Abstract (English)
Normative modelling is an increasingly common statistical technique in neuroimaging that estimates population-level benchmarks in brain structure. It enables the quantification of individual deviations from expected distributions whilst accounting for biological and technical covariates without requiring large, matched control groups. This makes it a powerful alternative to traditional case-control studies for identifying brain structural alterations associated with pathology. Despite the availability of numerous modelling approaches and several toolboxes with pre-trained models, the distinct strengths and limitations of normative modelling make it difficult to determine how and when to implement them appropriately. This review offers practical guidance and outlines statistical considerations for clinical researchers using normative modelling in neuroimaging. Through a worked example using clinical epilepsy data, we outline considerations for responsible implementation of pre-trained normative models, to support their broad and rigorous adoption in neuroimaging research.
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