Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

Area of Science:

• Neurology
• Neuroimaging
• Genetics

Background:

• Cerebral small vessel disease (cSVD) is a major cause of stroke, cognitive decline, and functional loss in the elderly.
• cSVD pathogenesis involves amyloidal/non-amyloidal subtypes, genetic factors, blood-brain barrier leakage, and endothelial dysfunction.

Purpose of the Study:

• To provide a comprehensive overview of cSVD, focusing on pathogenesis and biomarkers.
• To highlight recent advances in cSVD detection, prevention, and management.

Main Methods:

• Review of neuroimaging markers (white matter hyperintensities, lacunes, microbleeds, etc.).
• Exploration of genetic factors and pathophysiological mechanisms.
• Analysis of machine learning and deep learning applications in cSVD image processing.

Main Results:

• Neuroimaging markers are key for cSVD detection.
• Machine learning and deep learning enhance cSVD severity assessment.
• Emerging research links cSVD to Alzheimer's, Parkinson's, and non-cerebral diseases.

Conclusions:

• Understanding cSVD pathogenesis and biomarkers is crucial for early detection and intervention.
• Advanced computational strategies and understanding disease linkages can improve cSVD management.
• Further research into genetic and molecular mechanisms is needed.