Aug 2026· Neurology· Vol 107· 0 citations· 45 references
Medicine
TL;DR
Lesion composition may offer a more informative framework than global WMH burden for understanding cerebrovascular contributions to aging and neurodegeneration, with potential implications for risk stratification, clinical interpretation, and targeted interventions.
Abstract
Background and Objectives White matter hyperintensities (WMHs) are common neuroimaging markers of cerebrovascular pathology in aging and neurodegeneration. Despite their clinical relevance, WMH are typically quantified using global burden measures that assume a relatively homogeneous pathologic process. However, growing evidence suggests substantial biological heterogeneity across lesions. We aimed to identify lesion-level WMH subtypes beyond anatomic location and evaluate their associations with neurodegeneration and vascular risk. Methods We conducted a longitudinal observational study analyzing 3224 MRI scans from 403 participants spanning cognitively normal aging, mild cognitive impairment, Alzheimer, and Parkinson disease. Imaging at baseline and 2-year follow-up included structural, diffusion, and resting-state MRI. A total of 2107 WMH lesions were identified, and lesion-wise longitudinal changes were used to derive subtypes using unsupervised clustering. Associations with neurodegeneration and vascular risk factors were assessed using multivariable models with false discovery rate correction. Results Three lesion subtypes (L1–L3) were identified, frequently coexisting within the same individual. L1 lesions were the most prevalent (48.1%), predominated in cognitively normal individuals, and exhibited relatively stable trajectories without association with brain atrophy. L2 lesions represented a less frequent (11.3%) unstable subtype associated with weight gain (odds ratio [OR] 1.33, 95% CI 1.22–1.45; pFDR ≤ 0.001), suggesting metabolic vulnerability. L3 lesions (40.6%) represented an unstable subtype associated with brain atrophy (β = −0.11, 95% CI −0.16 to −0.05; pFDR < 0.001), older age (OR 1.15, 95% CI 1.07–1.23; pFDR < 0.001), and vascular risk reflected by pulse pressure changes (OR 1.09, 95% CI 1.03–1.15; pFDR = 0.006). Global WMH burden was no longer associated with brain atrophy after accounting for L3 lesion burden. Clustering robustness was supported by sensitivity analyses excluding anatomical location and by external validation in an independent cohort reproducing the main atrophy-related findings. Discussion WMH are not a homogeneous entity but comprise biologically distinct lesion subtypes with differential neurobiological and clinical significance. Lesion composition may therefore offer a more informative framework than global WMH burden for understanding cerebrovascular contributions to aging and neurodegeneration, with potential implications for risk stratification, clinical interpretation, and targeted interventions.
BACKGROUND: White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease but reflect late-stage injury. Diffusion magnetic resonance imaging (MRI) biomarkers have been proposed to capture earlier small vessel disease-related microstructural damage but their temporal progression relative to WMH and risk factors associated with progression remain unexplored. METHODS: We identified 2077 participants from the population-based cohort study of the Mayo Clinic Study of Aging in Olmsted County, Minnesota (aged 50–101 years) collected between 05/2005 and 09/2024 with longitudinal neuroimaging. Using multioutput nonlinear mixed-effects models in those with at least 2 fluid-attenuated inversion recovery-MRI and diffusion MRI scans, we characterized the temporal progression of WMH and 4 diffusion MRI biomarkers: fractional anisotropy of the genu of the corpus callosum, peak width of skeletonized mean diffusivity, free water, and Arteriolosclerosis-score, which were automatically estimated. Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, cardiometabolic conditions). RESULTS: The study population had a mean age of 78 years, 47% were women, 28% were APOE ε4 allele carriers, and 80% were cognitively unimpaired, with an average follow-up of 5.2 years (SD, 4.3 years) for fluid-attenuated inversion recovery-MRI and 4.3 years (SD, 3.9 years) for diffusion MRI. Arteriolosclerosis-score, fractional anisotropy of the genu of the corpus callosum, free water, and peak width of skeletonized mean diffusivity became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH become abnormal (half-width of CI <1 year), respectively. Global markers (Arteriolosclerosis-score, free water, peak width of skeletonized mean diffusivity, and WMH) were correlated, indicating shared substrates of widespread white matter injury. Fractional anisotropy of the genu of the corpus callosum, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Female participants showed earlier WMH, fractional anisotropy of the genu of the corpus callosum, and Arteriolosclerosis-score abnormalities, whereas male participants exhibited earlier peak width of skeletonized mean diffusivity and free water abnormalities. CONCLUSIONS: Diffusion MRI biomarkers were abnormal at least a decade before WMH become abnormal in the population, revealing a prolonged phase of early small vessel disease and highlighting their potential for small vessel disease prevention.
P. Vemuri, Mingzhao Hu, Emily S. Lundt et al.· Stroke· 0 citations
Vascular cognitive impairment and dementia (VCID) is a leading modifiable contributor to dementia, accounting for an estimated 27-33% of attributable dementia cases. Neuroimaging is central to detection, phenotyping, and longitudinal monitoring. This neuroradiology-focused review integrates the STRIVE-2 imaging lexicon with the VasCog-2 clinical framework and reviews the advanced MRI techniques most pertinent to VCID, spanning clinically established to emerging research approaches: arterial spin labelling, diffusion tensor imaging including peak width of skeletonised mean diffusivity (PSMD), quantitative susceptibility mapping, vessel architecture imaging, and resting-state functional MRI. Unlike previous reviews, we consolidate operational quantitative thresholds already informing decisions (the Staals total small-vessel-disease score, longitudinal white matter hyperintensity progression linked to dementia risk, microbleed and cortical superficial siderosis (cSS) criteria for anti-amyloid therapy eligibility, and validated PSMD reference ranges) and we critically address gaps between research-grade acquisitions and routine clinical workflows. Automated segmentation and structured reporting are transitioning into deployment, expanding the radiologist's role to combine pattern recognition with quantitative characterisation. We outline current limitations of standardisation, reproducibility, and external validation that remain prerequisites before quantitative VCID imaging achieves formal regulatory biomarker qualification.
Jose Federico Ojeda-Esparza, D. Botta, A. Fitsiori et al.· British Journal of Radiology· 0 citations
Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WMH), a multicontrast MRI machine learning framework that characterises white matter pathology through tissue microstructural information rather than lesion contrast alone. Methods: RADAR-WMH was trained on quantitative relaxometry and diffusion-weighted MRI acquired in community-dwelling participants (mean age 59.6 years [SD 22.4], 60.8% women, n=148) using a LightGBM classifier integrating local, textural, and anatomical features at the voxel level. Biological validity was assessed through longitudinal analyses and associations with age, cardio-vascular risk, and cognitive performance in independent cohorts. Results: RADAR-WMH achieved segmentation performance comparable to state-of-the-art FLAIR-based approaches without requiring FLAIR or T1-weighted data. Mean diffusivity was the most influential feature for lesion classification. Beyond FLAIR-defined WMH, RADAR-WMH identified tissue pathology extending outside lesion borders characterised by myelin loss, axonal injury, and increased extracellular water. These microstructural signatures persisted over follow-up and showed stronger association with age, systolic blood pressure, and cognitive performance than corresponding tissue properties restricted to FLAIR-defined WMH extent. Conclusions: RADAR-WMH reveals a significant burden of biologically meaningful white matter injury that remains invisible to FLAIR-defined WMH segmentation. By capturing microstructural pathology linked to vascular risk, cognitive decline, and lesion evolution, RADAR-WMH may provide more sensitive markers of cerebral small vessel disease than FLAIR-visible WMH alone.
V. Roduit, F. Carneiro, A. Lutti et al.· medRxiv· 0 citations
Background: Changes in ischemic white matter hyperintensities volume (WMH) on MRI over time are associated with cognitive decline. We investigated whether changes in WMH volume over time exhibit threshold effects of normalized WMH volume on declining cognitive performance and whether these effects on cognition differ between deep white matter hyperintensities (DWMH) and periventricular white matter hyperintensities (PVWMH). Methods: We followed 339 participants longitudinally from GeneSTAR with brain MRI and neuropsychological testing at baseline (2009–2013) and at 13-year follow-up (2023–present) (62% female, and 33% Black, mean baseline age 49.7±9.6). WMH were classified as PVWMH (within 2 mm of ventricles) or DWMH. Two-segment linear spline regression models using adjusted mixed linear regression identified test-specific thresholds longitudinally beyond which cognitive decline accelerated. Cognitive scores from both timepoints were treated as repeated measures, with WMH included as a time-varying predictor. Results: Declines in motor function and processing speed accelerated beyond thresholds of changing PVWMH and DWMH volumes. For Grooved Pegboard tests, changes in volume were associated with minimal effects below a threshold of changing volume (log-transformed ratio of lesion volume to intracranial volume for: PVWMH −9.42 to −9.29; and DWMH −11.8 to −11.7). Substantial declines in cognitive performance were observed above thresholds of increases in volume (slope differences: PVWMH; 14.5–15.1 seconds per log-unit, p < 0.001; and DWMH; 9.54–10.9, p < 0.001). Digit Symbol Substitution Test demonstrated paradoxical positive associations below changing volume thresholds (PVWMH; β=6.68, p=0.001 and DWMH; β=6.98, p < 0.001), reversing to decline above thresholds of increase in volume for PVWMH (Δβ=−11.2, p < 0.001) and DWMH (Δβ=−9.77, p < 0.001). Conclusion: Changes in WMH volume exhibit nonlinear threshold effects on changes in cognitive performance over time and differ by anatomic region. Minimal cognitive impact occurred below thresholds, with accelerated declines above. PVWMH demonstrate larger effects on declining cognitive function than DWMH, particularly for motor and processing speed functions and progress at a faster rate.
Sarvin Sasannia, M. Matsyuk, Shimeng Wang et al.· Stroke· 0 citations
Dementia arises from multifactorial neurodegenerative processes, with growing evidence implicating blood-brain barrier dysfunction, neuroinflammation, and vascular injury as contributors to progressive brain tissue damage. Because decline in white matter microstructural integrity is a prominent feature of brain aging, biomarkers that capture this process may improve dementia risk stratification beyond conventional clinical factors. Fractional anisotropy (FA), derived from diffusion tensor imaging, reflects white matter microstructural integrity and may provide a sensitive marker of downstream injury associated with chronic vascular and barrier dysfunction. We conducted a retrospective single-center study of 528 participants, including individuals with dementia (n = 176), age-matched cognitively normal older adults (n = 176), and a young healthy reference cohort (n = 176), all of whom underwent standardized 3-Tesla diffusion MRI. Dementia prediction was evaluated using elastic net-regularized logistic regression with nested tenfold cross-validation, incorporating 16 prespecified clinical and imaging predictors. Corpus callosum FA showed the largest standardized coefficient within the penalized model, exceeding all demographic and clinical variables. The model demonstrated strong discrimination between dementia and age-matched controls (area under the receiver operating characteristic curve [AUC] = 0.970; sensitivity = 90.9%; specificity = 93.2%). FA also declined stepwise from young adults to cognitively normal older adults to participants with dementia, consistent with progressive white matter microstructural injury across the aging-dementia continuum. These findings indicate that corpus callosum FA provides additional predictive value beyond conventional risk factors and support diffusion MRI as a potentially scalable biomarker of age-related white matter microstructural injury with potential utility for dementia risk stratification and biologically informed prevention strategies.
Shahar Negev, Benjamin F. Gruenbaum, V. Zvenigorodsky et al.· GeroScience· 0 citations
White matter hyperintensities (WMH) are a highly prevalent finding on FLAIR MRI scans and a prominent feature of white matter pathology across cerebrovascular and neurodegenerative diseases. Currently, WMH are assessed with visual rating scales such as the Fazekas scale or with their volume, as calculated from automatic or manual segmentations. Both methods have limitations: Visual rating scales are rater-dependent and coarse, while WMH volume does not take the confluence of lesions into account and thus disregards their spatial organisation. As an alternative, here we propose a novel automated method for quantifying the confluence of white matter hyperintensities on a continuous standardised scale between 0 and 1. The metric is based on WMH segmentations from routine MRI and quantifies the extent to which individual WMH merge into coherent lesions, independently of total lesion volume. We apply the method to QMIN-MC, a large UK memory clinic cohort, and show associations of the confluence metric with age, cognitive performance across domains, and Fazekas ratings. Participants with vascular and mixed dementia showed higher confluence than other diagnostic groups, whereas cognitively unimpaired participants showed lower confluence. However, confluence did not explain additional cognitive variance after accounting for log-transformed WMH volume. Findings were validated in DELCODE, an independent cohort of individuals with neurodegenerative disorders, replicating our original results. In this validation cohort, periventricular WMH confluence remained associated with cognition after adjustment for WMH volume. These findings introduce WMH confluence as a reproducible, automated, and fine-grained measure of lesion spatial organisation. It provides complementary information about morphological WMH severity beyond volume and is an alternative to visual rating scales. Although related to WMH volume in memory-clinic populations, confluence captures clinically interpretable information and may complement existing WMH measures for improved lesion characterisation in studies of white matter disease, ageing, and cognitive impairment.
Tatjana Schmidt, Robert Salzmann, M. Montagnese et al.· bioRxiv· 0 citations
