用数据驱动方法早期检测神经退行性疾病，助力个性化脑健康评估
Data-driven techniques for translational neuroscience and personalized neuro-health
- 基于多模态神经影像数据构建个体化分析模型
- 聚焦早期、微小且个体差异显著的脑变化特征
- 适合临床研究者与精准医疗开发者参考
阿尔茨海默病和帕金森病等神经退行性疾病通常在神经元大量不可逆损失后才被可靠诊断，亟需能从神经影像数据中定量捕捉早期、细微且个体特异脑变化的工具。本文综述了数据驱动技术在转化神经科学与个性化神经健康领域的广泛应用，围绕四大互补方法支柱展开。强调这些多样化方法共同致力于建立个性化、机制可解释、临床可操作的个体脑健康模型，并讨论当前在统计学、计算和临床应用方面仍面临的主要挑战。
原文摘要 · Abstract (English)
Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible, neuronal loss has already occurred, creating an urgent need for quantitative tools that can detect subtle, early, and individual-specific brain changes from neuroimaging data. This review surveys a broad and rapidly evolving toolkit of data-driven techniques for translational neuroscience and personalized neuro-health, organized around four complementary methodological pillars. Throughout, we emphasize how these methodologically diverse approaches converge on a common translational goal: personalized, mechanistically grounded, and clinically actionable models of individual brain health, and we close by discussing the principal open statistical, computational, and clinical challenges that remain.
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