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

Area of Science:

• Neuroscience
• Vascular Biology
• Biomedical Engineering

Background:

• Alzheimer's disease (AD) pathogenesis involves intricate links between cerebrovascular dysfunction, impaired glymphatic system function, and neuroinflammation.
• Vascular aging, marked by arterial stiffness and reduced cerebral blood flow (CBF), compromises the pulsatile forces essential for glymphatic clearance, leading to amyloid-beta (Aβ) buildup and cognitive decline.

Purpose of the Study:

• To review the mechanistic interplay between vascular aging, glymphatic impairment, and neuroinflammation in Alzheimer's disease.
• To explore the potential of machine learning (ML) tools in analyzing multimodal data for enhanced diagnostic precision in AD.

Main Methods:

• Literature review synthesizing current research on the interconnectedness of vascular, glymphatic, and inflammatory systems in AD.
• Exploration of machine learning applications, specifically DeepLabCut and Motion sequencing (MoSeq), for analyzing complex biological data.

Main Results:

• Vascular aging significantly disrupts glymphatic clearance, promoting Aβ accumulation and cognitive deficits.
• Machine learning tools show promise in integrating imaging and behavioral data to elucidate vascular-glymphatic dysfunction.

Conclusions:

• Understanding the crosstalk between vascular dysfunction, glymphatic impairment, and neuroinflammation is crucial for AD pathogenesis.
• Integrating advanced ML techniques with multimodal analyses offers a pathway to improved early diagnostics and targeted interventions for neurovascular health in AD.