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Abstract #3403

­­­­­­Location patterns of WMH in a multi-cohort study — generalisability and evaluation

Xin Zhao1,2, Ian Malone3, David Cash3,4, Andrew Wong1, Nish Chaturvedi1, Alun D. Hughes1,5, Jonathan M Schott3, Josephine Barnes3, and Carole H. Sudre1,3,4,5,6
1MRC Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom, 2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 3Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 4Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom, 5Institute of Cardiovascular Science, University College London, London, United Kingdom, 6School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom

Synopsis

Keywords: Other AI/ML, White Matter, Diagnosis/Prediction, Brain, Aging, Alzheimer's Disease, Analysis/Processing, Data Analysis, Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Multimodal, Neuro, Other Neurodegeneration

Motivation: The location distribution of white matter hyperintensities (WMH) may reflect different pathological processes but consistent characterisation across cohorts is challenging.

Goal(s): To develop and validate a framework for identifying WMH distribution patterns across multiple cohorts.

Approach: We employed unsupervised clustering to analyse WMH distribution in over 31,000 participants from ADNI3, Insight46, SABRE and UK Biobank, assessed its reproducibility, clinical relevance and predictive potential for progression.

Results: Two robust WMH location patterns with distinct clinical profiles were identified. They demonstrated utility in WMH progression prediction.

Impact: This framework provides a reproducible solution for identifying WMH patterns in over 31,000 participants across 4 cohorts. By characterising WMH distribution and progression, it informs future research into ageing and neurological disease pathways, and enhances the foundation for personalised care.

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Keywords