{
  "abstract": "Spinal cord morphometry provides essential biomarkers of neurological health, but clinical interpretations are confounded by inter-subject variability and a lack of normative references across the full human lifespan. We address this gap by generating the first comprehensive lifespan charts for cervical spinal cord morphometry. From 78,269 T1-weighted brain MRI sessions, we identified a final cross-sectional cohort of 24,251 healthy or cognitively normal controls from 33 datasets (ages 0-100 years) with cervical cord coverage. To overcome contrast variability, we employed a state-of-the-art contrast-agnostic deep learning segmentation method, extracting cross-sectional area (CSA), anteroposterior (AP) and right-left (RL/transverse), and shape indices (AP/RL ratio, eccentricity, and solidity) from C1 to C7. Normative trajectories were modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The resulting charts reveal distinct non-linear lifespan changes: rapid growth through childhood and adolescence, peak maturation occurring in early-to-mid adulthood (e.g., mid-30s for CSA), followed by gradual decreases. Significant regional variations along the cervical cord and consistent sex differences (males > females for size metrics) were quantified. Spinal cord trajectories showed strong temporal coupling with brain white matter and brainstem volumes, suggesting integrated CNS development and aging. These lifespan charts provide a robust normative framework, enabling age- and sex-specific centile scoring of individual spinal cord morphometry. This resource offers a critical tool for differentiating typical variation from pathological changes, enhancing the clinical utility of spinal cord MRI in studies of development and neurodegeneration.",
  "abstract_shown": true,
  "affiliations": [
    "Vanderbilt University Institute of Imaging Science"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 38,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "K. G. Schilling"
    },
    {
      "name": "M. E. Kim"
    },
    {
      "name": "M. Amandola"
    },
    {
      "name": "C. Gao"
    },
    {
      "name": "K. Ramadass"
    },
    {
      "name": "P. Kanakaraj"
    },
    {
      "name": "S. Bogdanov"
    },
    {
      "name": "G. Rudravaram"
    },
    {
      "name": "N. R. Newlin"
    },
    {
      "name": "D. Archer"
    },
    {
      "name": "T. J. Hohman"
    },
    {
      "name": "A. L. Jefferson"
    },
    {
      "name": "V. L. Morgan"
    },
    {
      "name": "A. Roche"
    },
    {
      "name": "D. J. Englot"
    },
    {
      "name": "M. Bilgel"
    },
    {
      "name": "L. L. Beason Held"
    },
    {
      "name": "L. Ferrucci"
    },
    {
      "name": "L. Cutting"
    },
    {
      "name": "L. A. Barquero"
    },
    {
      "name": "M. A. Darchangel"
    },
    {
      "name": "T. Q. Nguyen"
    },
    {
      "name": "K. L. Humphreys"
    },
    {
      "name": "Y. Niu"
    },
    {
      "name": "S. Vinci-Booher"
    },
    {
      "name": "C. J. Cascio"
    },
    {
      "name": "The HABS-HD Study Team"
    },
    {
      "name": "Alzheimers Disease Neuroimaging Initiative"
    },
    {
      "name": "The BIOCARD Study Team"
    },
    {
      "name": "Z. Li"
    },
    {
      "name": "D. Moyer"
    },
    {
      "name": "S. N. Vandekar"
    },
    {
      "name": "P. Zhang"
    },
    {
      "name": "S. St-Onge"
    },
    {
      "name": "S. Bedard"
    },
    {
      "name": "J. Valosek"
    },
    {
      "name": "B. De Leener"
    },
    {
      "name": "J. Cohen-Adad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/77kydg9p8d46f.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.06.03.729823",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.06.03.729823v2.full",
  "id": "77kydg9p8d46f",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.06.03.729823v2",
  "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",
      "2204|Medical Imaging and Analysis"
    ]
  ],
  "openalex": "W7163918472",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.06.03.729823v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/77kydg9p8d46f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:37Z",
      "native_id": "10.64898/2026.06.03.729823",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.06.03.729823v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Charting Cervical Spinal Cord Morphometry Across the Lifespan",
  "title_text": "Charting Cervical Spinal Cord Morphometry Across the Lifespan",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:37Z",
  "url": "https://research.draconitic.com/paper/77kydg9p8d46f",
  "venue": "bioRxiv",
  "versions": []
}