Mapping the brain-web morphology to characterise ageing and cognitive impairment
Year: 2026
Type: preprint
Abstract: Ageing reshapes human brain anatomy across spatial scales, yet its geometrical organisation remains difficult to quantify. Here we introduce SHARP (Spectral Hierarchy Analysis, Reconstruction and Prediction), which transfers hierarchical cosmic-web classification to structural MRI, identifying knots, filaments, sheets and voids at four spectral levels and combining their morphology with higher-order spatial statistics and anatomical measurements. In CamCAN, selected filament- and sheet-associated features show opposing correlations with age and distinct associations with subcortical and ventricular volumes. To quantify their contribution, we compare SHARP with a neural-network baseline combining 36 regional anatomical measurements, total brain volume and sex. Adding cosmologically motivated descriptors reduces the mean absolute error of MRI-only age prediction from 6.04 to 4.71 years (22%) in CamCAN and from 4.57 to 4.07 years (11%) in OASIS-3 under participant-independent validation. Brain-web morphology with only brain volume and sex already achieves 5.12 years in CamCAN. For cognitive-status classification in OASIS-3, SHARP identifies 85% of Clinical Dementia Rating (CDR)-positive observations at 86% specificity, compared with 78% sensitivity for the anatomical baseline at matched empirical specificity. These results demonstrate that cosmological methods provide complementary geometrical information and measurable gains in characterising brain ageing and cognitive impairment.
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225613321
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