Location patterns and longitudinal progression of white matter hyperintensities

Area of Science:

• Neuroimaging and computational analysis of brain structure.
• Cerebrovascular disease research and risk factor identification.
• Development of data-driven phenotyping methods.

Background:

• White matter hyperintensities (WMH) are key indicators of cerebral small vessel disease (CSVD).
• Spatial heterogeneity of WMH may correlate with distinct clinical presentations.
• Previous methods like principal component analysis have limitations in stratifying WMH subtypes.

Purpose of the Study:

• To develop a data-driven framework for identifying spatial WMH subtypes.
• To characterize the demographic and clinical profiles associated with these subtypes.
• To assess the predictive value of WMH spatial subtypes for future WMH progression.

Main Methods:

• Analysis of MRI scans from over 63,000 individuals across four major cohorts.
• Automated WMH segmentation and regional quantification using a 36-region framework.
• Application of clustering and stability-based approaches to identify WMH subtypes and their associations with risk factors; validation in an independent cohort.

Main Results:

• Five distinct WMH spatial patterns were identified and validated, showing varied lesion burdens and distributions.
• These patterns demonstrated differential associations with demographic, vascular, metabolic, inflammatory, and genetic risk factors.
• Spatial WMH subtypes, particularly regional volumes, provided superior prediction of WMH progression compared to global volume alone.

Conclusions:

• A robust and scalable framework for spatial WMH phenotyping was established.
• WMH spatial characterization offers prognostic implications beyond total lesion burden.
• Findings support the utility of spatial WMH analysis for risk stratification and personalized CSVD management.