血管和类淋巴系统功能障碍作为阿尔茨海默病认知障碍的驱动因素：来自计算方法的见解

Vascular and glymphatic dysfunction as drivers of cognitive impairment in Alzheimer's disease: Insights from computational approaches.

作者信息

Fatima Gehan, Ashiquzzaman Akm, Kim Sang Seong, Kim Young Ro, Kwon Hyuk-Sang, Chung Euiheon

机构信息

Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Rep. of Korea.

Department of Radiology, Harvard Medical School, Boston, MA 02115, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.

出版信息

Neurobiol Dis. 2025 May;208:106877. doi: 10.1016/j.nbd.2025.106877. Epub 2025 Mar 17.

DOI:10.1016/j.nbd.2025.106877

PMID:40107629

Abstract

Alzheimer's disease (AD) is driven by complex interactions between vascular dysfunction, glymphatic system impairment, and neuroinflammation. Vascular aging, characterized by arterial stiffness and reduced cerebral blood flow (CBF), disrupts the pulsatile forces necessary for glymphatic clearance, exacerbating amyloid-beta (Aβ) accumulation and cognitive decline. This review synthesizes insights into the mechanistic crosstalk between these systems and explores their contributions to AD pathogenesis. Emerging machine learning (ML) tools, such as DeepLabCut and Motion sequencing (MoSeq), offer innovative solutions for analyzing multimodal data and enhancing diagnostic precision. Integrating ML with imaging and behavioral analyses bridges gaps in understanding vascular-glymphatic dysfunction. Future research must prioritize these interactions to develop early diagnostics and targeted interventions, advancing our understanding of neurovascular health in AD.

摘要

阿尔茨海默病（AD）是由血管功能障碍、类淋巴系统损伤和神经炎症之间的复杂相互作用所驱动的。以动脉僵硬度增加和脑血流量（CBF）减少为特征的血管老化，破坏了类淋巴清除所需的搏动力量，加剧了β-淀粉样蛋白（Aβ）的积累和认知衰退。本综述综合了对这些系统之间机制性相互作用的见解，并探讨了它们对AD发病机制的贡献。新兴的机器学习（ML）工具，如DeepLabCut和运动序列分析（MoSeq），为分析多模态数据和提高诊断精度提供了创新解决方案。将ML与成像和行为分析相结合，弥合了在理解血管-类淋巴功能障碍方面的差距。未来的研究必须优先考虑这些相互作用，以开发早期诊断方法和靶向干预措施，增进我们对AD中神经血管健康的理解。