Multimodal Magnetic Resonance Imaging Signatures of White Matter Hyperintensities: Mechanistic Insights Into Pathobiological Heterogeneity

다국어 초록 (Multilingual Abstract)

White matter hyperintensity (WMH), a common magnetic resonance imaging (MRI) marker of cerebral small-vessel disease, is associated with chronic cerebral ischemia; however, the mechanistic heterogeneity of WMH remains poorly defined. This review integrates multimodal MRI findings into a mechanism-oriented framework spanning four axes: WMH versus normalappearing white matter (NAWM), periventricular versus deep location, lesion core versus perilesional penumbra, and longitudinal evolution. Periventricular WMHs are associated with blood–brain barrier dysfunction, interstitial fluid accumulation, and venous remodeling, whereas deep WMHs are more closely associated with impaired glymphatic/perivascular clearance and enlarged perivascular spaces, and demyelination/macromolecular compromise varying by context. The perilesional penumbra emerges as a critical transition zone, showing distance-dependent gradients of microstructural rarefaction, extracellular fluid expansion, perfusion deficits, and reduced vascular reactivity that extend beyond fluid-attenuated inversion recovery-defined borders and relate to subsequent lesion growth. Longitudinal data further indicate that abnormalities in diffusion, perfusion, and vascular reserve within NAWM precede new WMHs, nominating imaging biomarkers of progression risk. This framework supports risk stratification beyond total lesion burden, links therapeutic opportunities to mechanism (e.g., blood–brain barrier integrity, glymphatic clearance, and cerebrovascular reactivity), and motivates biologically interpretable readouts for patient selection and treatment monitoring. Looking forward, standardized spatial classification (including fine-grained, distance-informed parcellations), harmonized penumbra definitions, and integration of multimodal MRI with pathology will be essential to validate mechanism-specific subtypes and translate them into scalable, clinically usable endpoints.