{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Universidad de La Laguna"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "F.-S. Kitaura"
    },
    {
      "name": "A. Carnero-Rosell"
    },
    {
      "name": "J. L. Sosa Ysalguez"
    },
    {
      "name": "D. Anido Pereiro"
    },
    {
      "name": "F. Sinigaglia"
    },
    {
      "name": "N. Janssen"
    },
    {
      "name": "A. Maselli"
    },
    {
      "name": "E. Pereda"
    },
    {
      "name": "M. Huertas Company"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/498r2sj14f62t.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.06.764800",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764800v1.full",
  "id": "498r2sj14f62t",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764800v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764800v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/498r2sj14f62t.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:21:46Z",
      "native_id": "10.64898/2026.11.06.764800",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764800v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Mapping the brain-web morphology to characterise ageing and cognitive impairment",
  "title_text": "Mapping the brain-web morphology to characterise ageing and cognitive impairment",
  "type": "preprint",
  "updated_at": "2026-11-09T12:21:46Z",
  "url": "https://research.draconitic.com/paper/498r2sj14f62t",
  "venue": "bioRxiv",
  "versions": []
}