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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