{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:00:47Z","timestamp":1772024447622,"version":"3.50.1"},"posted":{"date-parts":[[2026,2,23]]},"group-title":"Radiology and Imaging","reference-count":50,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,2,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background and Objectives<\/jats:title>\n                  <jats:p>White matter hyperintensities (WMH) of presumed vascular origin are a neuroimaging hallmark of cerebral small vessel disease (CSVD). Their spatial heterogeneity may reflect different clinical phenotypes. Most prior studies relied on principal component analysis to characterise such heterogeneity, which has limited ability to stratify individuals into discrete and interpretable WMH subtypes. We therefore propose a data-driven framework to identify WMH spatial subtypes, characterise their demographic and clinical profiles, and investigate their predictive value for future WMH progression.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We analysed MRI scans from 63,338 individuals across 4 major cohorts (internal data): ADNI3, Insight46, SABRE and UK Biobank (UKB), and validated our findings in the OASIS-3 dataset (n=844). WMH were automatically segmented and regionally quantified using a 36-region bullseye framework. Clustering was applied to the relative regional distributions of WMH. A stability-based approach was used to identify robust WMH subtypes. Their associations with 19 risk factors of interest were analysed using multivariable regression. In a subset with follow-up MRI scans (internal: n=5,274, OASIS-3: n=182), we evaluated the predictive value of these subtypes combined with other volumetric or spatial WMH variables for WMH progression.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Five WMH location patterns with different lesion burden and spatial distribution were identified (stability score 0.946) and reproduced in OASIS-3. These patterns showed distinct associations with demographic, vascular, metabolic, inflammatory and genetic risk factors. Higher-burden patterns were independently associated with older age, higher blood pressure, diabetes and smoking, indicating a gradient of vascular risk across spatial subtypes. WMH location patterns were largely preserved over 18-30 months, with most individuals remaining within the same pattern (71.5%). While global baseline WMH volume remained a strong predictor of future WMH progression (balanced accuracy 0.693, 95% CI: 0.664-0.723), models including baseline regional WMH volumes consistently outperformed other candidates (best balanced accuracy 0.737, 95% CI: 0.706-0.764).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>We presented a robust and scalable framework for spatial WMH phenotyping. We discussed clinical and prognostic implications of the spatial subtypes beyond total lesion burden. Our findings supported the value of WMH spatial characterisation in stratifying risk that may help guide personalised approaches to managing CSVD.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.02.20.26346709","type":"posted-content","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T01:30:17Z","timestamp":1771896617000},"source":"Crossref","is-referenced-by-count":0,"title":["Location patterns and longitudinal progression of white matter hyperintensities"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5703-6145","authenticated-orcid":false,"given":"Xin","family":"Zhao","sequence":"first","affiliation":[{"name":"Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom"},{"name":"Department of Medical Physics and Biomedical Engineering, University College London, London, United 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University College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7833-616X","authenticated-orcid":false,"given":"David M.","family":"Cash","sequence":"additional","affiliation":[{"name":"Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom"},{"name":"UK Dementia Research Institute, University College London, London, United Kingdom"},{"name":"Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6211-2775","authenticated-orcid":false,"given":"Nish","family":"Chaturvedi","sequence":"additional","affiliation":[{"name":"Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United 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