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Peer-reviewedCC BYHuman brain mapping

Charting Cervical Spinal Cord Morphometry Across the Lifespan.

  • Kurt G Schilling1,2
  • Michael E Kim3
  • Matthew Amandola1
  • Chenyu Gao4
  • Karthik Ramadass3,4
  • Praitayini Kanakaraj3
  • Sam Bogdanov5
  • Gaurav Rudravaram4
  • Nancy R Newlin3
  • Derek Archer6,7,8
  • Timothy J Hohman6,7,9
  • Angela L Jefferson6,9,10,11,12,13
29 more authors
  • Victoria L Morgan1,2,14
  • Alexandra Roche2
  • Dario J Englot1,3,4,14,15
  • Murat Bilgel16
  • Lori L Beason Held16
  • Luigi Ferrucci16
  • Laurie E Cutting8,17
  • Laura A Barquero17
  • Micah A D'archangel17
  • Tin Q Nguyen2,8,17
  • Kathryn L Humphreys12
  • Yanbin Niu12
  • Sophia Vinci-Booher12
  • Carissa J Cascio18
  • HABS‐HD Study Team
  • Alzheimer's Disease Neuroimaging Initiative
  • BIOCARD Study Team
  • Zhiyuan Li19
  • Daniel Moyer3,4
  • Simon N Vandekar20
  • Panpan Zhang6,9,20
  • Samuelle St-Onge21,22
  • Sandrine Bédard21
  • Jan Valošek21,23,24,25
  • Benjamin De Leener21,26,27
  • Julien Cohen-Adad21,28,29
  • John C Gore1,2,14
  • Seth Smith1,2,4,14
  • Bennett A Landman1,2,3,4,8,9,11,12,13,14,20
29 affiliations
  1. 1Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  2. 2Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA.
  3. 3Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
  4. 4Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA.
  5. 5Medical Scientist Training Program, Vanderbilt University, Nashville, Tennessee, USA.
  6. 6Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer's Center, Nashville, Tennessee, USA.
  7. 7Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, USA.
  8. 8Vanderbilt University, Vanderbilt Brain Institute, Nashville, Tennessee, USA.
  9. 9Vanderbilt Alzheimer's Disease Research Center, Nashville, Tennessee, USA.
  10. 10Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  11. 11Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  12. 12Department of Psychology and Human Development, Vanderbilt University, Nashville, Tennessee, USA.
  13. 13Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  14. 14Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
  15. 15Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  16. 16Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA.
  17. 17Department of Special Education, Peabody College of Education and Human Development, Nashville, Tennessee, USA.
  18. 18Life Span Institute and Department of Psychology, University of Kansas, Lawrence, Kansas, USA.
  19. 19Department of Computer Science, Park University, Parkville, Missouri, USA.
  20. 20Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  21. 21NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada.
  22. 22CHU Sainte-Justine Research Centre, Montréal, Quebec, Canada.
  23. 23Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland.
  24. 24Departments of Neurosurgery and Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia.
  25. 25Department of Biomedical Engineering, University Hospital Olomouc, Olomouc, Czechia.
  26. 26Centre de recherche du CHU Sainte-Justine, Montréal, Canada.
  27. 27Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montréal, Canada.
  28. 28Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Quebec, Canada.
  29. 29Mila-Quebec AI Institute, Montreal, Quebec, Canada.
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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.

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Cite

BibTeXRIS
BibTeX
@article{schilling2026charting,
  title = {{Charting Cervical Spinal Cord Morphometry Across the Lifespan.}},
  author = {Kurt G Schilling and Michael E Kim and Matthew Amandola and Chenyu Gao and Karthik Ramadass and Praitayini Kanakaraj and Sam Bogdanov and Gaurav Rudravaram and Nancy R Newlin and Derek Archer and Timothy J Hohman and Angela L Jefferson and Victoria L Morgan and Alexandra Roche and Dario J Englot and Murat Bilgel and Lori L Beason Held and Luigi Ferrucci and Laurie E Cutting and Laura A Barquero and Micah A D'archangel and Tin Q Nguyen and Kathryn L Humphreys and Yanbin Niu and Sophia Vinci-Booher and Carissa J Cascio and HABS‐HD Study Team and Alzheimer's Disease Neuroimaging Initiative and BIOCARD Study Team and Zhiyuan Li and Daniel Moyer and Simon N Vandekar and Panpan Zhang and Samuelle St-Onge and Sandrine Bédard and Jan Valošek and Benjamin De Leener and Julien Cohen-Adad and John C Gore and Seth Smith and Bennett A Landman},
  journal = {Human brain mapping},
  year = {2026},
  month = oct,
  doi = {10.1002/hbm.70658},
  url = {https://europepmc.org/article/MED/42908281},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Charting Cervical Spinal Cord Morphometry Across the Lifespan.
AU  - Kurt G Schilling
AU  - Michael E Kim
AU  - Matthew Amandola
AU  - Chenyu Gao
AU  - Karthik Ramadass
AU  - Praitayini Kanakaraj
AU  - Sam Bogdanov
AU  - Gaurav Rudravaram
AU  - Nancy R Newlin
AU  - Derek Archer
AU  - Timothy J Hohman
AU  - Angela L Jefferson
AU  - Victoria L Morgan
AU  - Alexandra Roche
AU  - Dario J Englot
AU  - Murat Bilgel
AU  - Lori L Beason Held
AU  - Luigi Ferrucci
AU  - Laurie E Cutting
AU  - Laura A Barquero
AU  - Micah A D'archangel
AU  - Tin Q Nguyen
AU  - Kathryn L Humphreys
AU  - Yanbin Niu
AU  - Sophia Vinci-Booher
AU  - Carissa J Cascio
AU  - HABS‐HD Study Team
AU  - Alzheimer's Disease Neuroimaging Initiative
AU  - BIOCARD Study Team
AU  - Zhiyuan Li
AU  - Daniel Moyer
AU  - Simon N Vandekar
AU  - Panpan Zhang
AU  - Samuelle St-Onge
AU  - Sandrine Bédard
AU  - Jan Valošek
AU  - Benjamin De Leener
AU  - Julien Cohen-Adad
AU  - John C Gore
AU  - Seth Smith
AU  - Bennett A Landman
PY  - 2026
DA  - 2026/10/01
JO  - Human brain mapping
DO  - 10.1002/hbm.70658
UR  - https://europepmc.org/article/MED/42908281
AB  - 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.
SN  - 1065-9471
SN  - 1097-0193
ER  -