Connecting the Dots: Linking White Matter Hyperintensity Patterns to Longitudinal Cognitive Changes in Ageing in The Irish Longitudinal Study on Ageing (TILDA) - Courtney, Michael (Trinity College Dublin. School of Medicine. Discipline of Clinical Medicine, 2025)
INTRODUCTION White matter hyperintensities (WMHs) are known to correlate with cognitive decline, stroke, and dementia. Previous research has explored the independent effects of white matter macrostructure, microstructure, and spatial distribution on cognitive function, yet a comprehensive analysis combining these elements is limited. This thesis stratifies WMHs into six distinct phenotypes based on location, volume, and microstructural properties in order to assess their association with cognitive decline over a six-year period. METHODS Data was obtained from The Irish Longitudinal Study on Ageing (TILDA), including MRI scans and cognitive performance scores from 497 community-dwelling older adults. WMHs were segmented using the Lesion Prediction Algorithm (LPA) and analysed with fractional anisotropy (FA) and mean diffusivity (MD). K-means clustering identified three primary WMH phenotypes, further stratified by periventricular and deep white matter location, resulting in six lesion phenotypes. Linear mixed effect models were used to assess the relationship between these phenotypes and cognitive decline, adjusting for demographic and health-related variables. RESULTS High-volume, low FA WMHs in both deep (p=0.05) and periventricular (p=0.004) white matter were significantly linked to cognitive decline over six years (X=12.0, P=0.004). Deep lesions, irrespective of FA or volume, were linked to poorer cognitive performance. Small periventricular lesion with near normal microstructural properties do not predict cognitive decline CONCLUSIONS This study provides evidence that WMHs manifest as diverse phenotypes, with specific lesion types associated with cognitive decline. High-volume, low FA lesions in both periventricular and deep white matter are particularly predictive of cognitive deterioration. Identifying WMH phenotypes may inform early intervention strategies and improve patient outcomes by targeting individuals at higher risk of cognitive decline.
APPROVED