Topographic reference modelling of white matter hyperintensities across the adult lifespan

Alshikho, M. J.; Pyne, J. D.; Edwards, N. C.; Hale, C.; Samuel, D. C.; Morales, C. D.; Morel, N.; Lippert, R. V.; Archer, D. B.; Cuccaro, M. L.; Toga, A. W.; Saykin, A. J.; Risacher, S. L.; Belloy, M. E.; Kukull, W.; Biber, S.; Mez, J.; Crane, P. K.; Mukherjee, S.; Erus, G.; Sperling, R. A.; Bennett, D. A.; Barnes, L. L.; Schneider, J. A.; Johnson, S. C.; Lee, A. J.; Davatzikos, C.; O'Bryant,, S.; Barber, R.; Cruchaga, C.; Benzinger, T. L. S.; Lee, D. Y.; Yi, D.; A4 Study team,; HABS-HD Study Team,; Alzheimers Disease Neuroimaging Initiative (ADNI),; Alzheimers Disease Sequencing Project

doi:10.64898/2026.10.28.760476

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bioRxiv · 10.64898/2026.10.28.760476

Topographic reference modelling of white matter hyperintensities across the adult lifespan

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Abstract

White matter hyperintensities (WMH) are among the most common MRI findings in older adults. They are widely regarded as markers of brain aging and cerebral small-vessel disease, and associated with risk for cognitive decline, Alzheimers disease and related dementias (ADRD), and stroke. White matter hyperintensities (WMH) are typically interpreted by total lesion burden, but lesions with similar volumes can occupy different anatomical locations, potentially reflecting distinct pathological processes. Here we present voxelwise topographic reference modelling (vTRM), a framework for characterizing individual WMH masks against age- and sex-stratified frequency maps derived from cognitively unimpaired individuals. Using 42,469 FLAIR-derived lesion masks from twelve adult cohorts, we mapped population-level WMH spatial vulnerability, characterized age-related changes in its distribution across periventricular, lobar and arterial-territory compartments, and constructed paired percentile charts for lesion burden and spatial typicality. A quadrant-based classification defined by these two percentile dimensions revealed graded relationships with ADRD pathophysiology, neurodegeneration, and cognitive impairment, while identifying WMH patterns differentially associated with ageing-related, vascular and neurodegenerative processes among individuals with similar lesion volumes. These reference charts establish a benchmark for normative WMH ageing, enabling quantification of individual deviations from typical age-, sex- and burden-related patterns and identification of WMH phenotypes associated with vascular, neurodegenerative or mixed etiologies. The framework, together with a deployable 3D U-Net model trained on these data for automated WMH segmentation, is available through the Brain Lesion Analytics platform (https://brainlesionanalytics.org/).

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Alshikho, M. J., Pyne, J. D., Edwards, N. C., Hale, C., Samuel, D. C., Morales, C. D., Morel, N., Lippert, R. V., Archer, D. B., Cuccaro, M. L., Toga, A. W., Saykin, A. J., Risacher, S. L., Belloy, M. E., Kukull, W., Biber, S., Mez, J., Crane, P. K., Mukherjee, S., Erus, G., Sperling, R. A., Bennett, D. A., Barnes, L. L., Schneider, J. A., Johnson, S. C., Lee, A. J., Davatzikos, C., O'Bryant,, S., Barber, R., Cruchaga, C., Benzinger, T. L. S., Lee, D. Y., Yi, D., A4 Study team,, HABS-HD Study Team,, Alzheimers Disease Neuroimaging Initiative (ADNI),, Alzheimers Disease Sequencing Project. 2026-11-03. Topographic reference modelling of white matter hyperintensities across the adult lifespan. https://doi.org/10.64898/2026.10.28.760476

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