转化神经科学与个性化神经健康的数据驱动技术
Data-driven techniques for translational neuroscience and personalized neuro-health
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中文总结 AI 辅助
针对神经退行性疾病确诊滞后的问题，该综述梳理四类数据驱动技术，旨在构建个性化脑健康模型并探讨相关挑战。
中文摘要 AI 辅助
阿尔茨海默病、帕金森病等神经退行性疾病只有在发生大量、往往不可逆的神经元丢失后才能最可靠地确诊，因此迫切需要能从神经影像数据中检测细微、早期且个体特异性脑变化的定量工具。本综述梳理了用于转化神经科学与个性化神经健康的广泛且快速发展的数据驱动技术工具集，围绕四个互补的方法学支柱组织。全程强调这些方法多样的方法学途径如何汇聚到共同的转化目标：构建个性化、有机制依据且临床可操作的个体脑健康模型，最后讨论仍存在的主要统计、计算和临床挑战。
英文摘要
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.
发表机构
- University of Maryland Baltimore County（马里兰大学巴尔的摩郡分校）
- Texas Tech University（得克萨斯理工大学）
- University of Minnesota（明尼苏达大学）
- University of Nebraska Lincoln（内布拉斯加大学林肯分校）
机构由 AI 辅助整理，请以论文原文为准。
