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Peer-reviewedCC BYNature Communications

The genetic architecture of cortical similarity networks

  • Isaac Sebenius1,2,3,4
  • Varun Warrier5,6,4
  • Richard A. I. Bethlehem6,4
  • Richard Dear5,4
  • Eva-Maria Stauffer5,4
  • Yuanjun Gu7,5,4
  • Rafael Romero-García5,8,9
  • Jakob Seidlitz10,11,12,13
  • Edward Thomas Bullmore5,14,4
  • Sarah Elizabeth Morgan15,16,17
17 affiliations
  1. 1Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom. [email protected]
  2. 2Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom. [email protected]
  3. 3Harvard Medical School, Boston, MA, US. [email protected]
  4. 4University of Cambridge
  5. 5Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
  6. 6Department of Psychology, University of Cambridge, Cambridge, United Kingdom
  7. 7Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
  8. 8Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Departamento de Fisiología Médica y Biofísica, Seville, Spain
  9. 9Universidad de Sevilla
  10. 10Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
  11. 11Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
  12. 12Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
  13. 13University of Pennsylvania
  14. 14School of Academic Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
  15. 15Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
  16. 16School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
  17. 17King's College London
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Abstract

The genetic architecture of human brain networks is central to understanding cortical organisation and evolution, the causal links between brain structure and function, and the pathogenesis of neuropsychiatric disorders. Using N > 48,000 subjects, we investigated common genetic effects on Morphometric INverse Divergence (MIND), a heritable, multi-modal structural MRI metric of inter-areal similarity and connectivity. Genetic correlations between MIND network edges were largely reducible to two gradients, each aligned with distance from one of the two phylogenetically primitive areas (paleocortex and archicortex) predicted by the dual origin theory of cortical evolution. MIND was more heritable than comparable measures of functional (f)MRI connectivity, and the paleocortically-aligned MIND gradient was genetically correlated with, and causally predictive of, fMRI connectivity. Finally, we identified genetic overlaps between MIND gradients and neuropsychiatric and biomedical traits. These results provide fresh insight into the dual origins of the cortex and their implications for brain function and health.

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Cite

BibTeXRIS
BibTeX
@article{sebenius2026genetic,
  title = {{The genetic architecture of cortical similarity networks}},
  author = {Isaac Sebenius and Varun Warrier and Richard A. I. Bethlehem and Richard Dear and Eva-Maria Stauffer and Yuanjun Gu and Rafael Romero-García and Jakob Seidlitz and Edward Thomas Bullmore and Sarah Elizabeth Morgan},
  journal = {Nature Communications},
  year = {2026},
  month = jun,
  doi = {10.1038/s41467-026-73714-9},
  url = {https://doi.org/10.1038/s41467-026-73714-9},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - The genetic architecture of cortical similarity networks
AU  - Isaac Sebenius
AU  - Varun Warrier
AU  - Richard A. I. Bethlehem
AU  - Richard Dear
AU  - Eva-Maria Stauffer
AU  - Yuanjun Gu
AU  - Rafael Romero-García
AU  - Jakob Seidlitz
AU  - Edward Thomas Bullmore
AU  - Sarah Elizabeth Morgan
PY  - 2026
DA  - 2026/06/20
JO  - Nature Communications
DO  - 10.1038/s41467-026-73714-9
UR  - https://doi.org/10.1038/s41467-026-73714-9
AB  - The genetic architecture of human brain networks is central to understanding cortical organisation and evolution, the causal links between brain structure and function, and the pathogenesis of neuropsychiatric disorders. Using N > 48,000 subjects, we investigated common genetic effects on Morphometric INverse Divergence (MIND), a heritable, multi-modal structural MRI metric of inter-areal similarity and connectivity. Genetic correlations between MIND network edges were largely reducible to two gradients, each aligned with distance from one of the two phylogenetically primitive areas (paleocortex and archicortex) predicted by the dual origin theory of cortical evolution. MIND was more heritable than comparable measures of functional (f)MRI connectivity, and the paleocortically-aligned MIND gradient was genetically correlated with, and causally predictive of, fMRI connectivity. Finally, we identified genetic overlaps between MIND gradients and neuropsychiatric and biomedical traits. These results provide fresh insight into the dual origins of the cortex and their implications for brain function and health.
SN  - 2041-1723
ER  -