突破单一数据对比局限，用多模态纵向数据找脑病生物标志物
Pursuit of biomarkers of brain diseases: Beyond cohort comparisons
- 提出脑部数据交换思想实验，揭示传统对比方法的内在缺陷
- 强调需融合活动、神经递质、影像等多模态长期数据构建生物标志物
- 适合临床研究者与跨模态神经科学工作者参考
尽管已有大量脑数据和先进的AI分析算法，脑特征在临床诊断与预后中仍鲜有应用。本文指出，领域仍依赖患者与健康对照的群体比较来寻找生物标志物，却忽视了脑特征固有的退化性问题。通过一个思想实验（脑部数据交换），我们证明：仅增加数据量或提升算法能力，无法解决生物标志物识别难题。建议不应仅用单一数据类型进行患者与对照比较，而应结合多模态（如脑活动、神经递质、神经调节物质、脑成像）及纵向数据，在分组前就引导分析，从而定义多维脑病生物标志物。
原文摘要 · Abstract (English)
Despite the diversity and volume of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment (Brain Swap), we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patient versus healthy controls using single data type, we should use multimodal (e.g. brain activity, neurotransmitters, neuromodulators, brain imaging) and longitudinal brain data to guide the grouping before defining multidimensional biomarkers for brain diseases.
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