Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer’s Disease and Neurodegeneration stratified by sex

  1. Irene Cumplido-Mayoral
  2. Marina García-Prat
  3. Grégory Operto
  4. Carles Falcon
  5. Mahnaz Shekari
  6. Raffaele Cacciaglia
  7. Marta Milà-Alomà
  8. Luigi Lorenzini
  9. Silvia Ingala
  10. Alle Meije Wink
  11. Henk JMM Mutsaerts
  12. Carolina Minguillón
  13. Karine Fauria
  14. José Luis Molinuevo
  15. Sven Haller
  16. Gael Chetelat
  17. Adam Waldman
  18. Adam Schwarz
  19. Frederik Barkhof
  20. Ivonne Suridjan
  21. Gwendlyn Kollmorgen
  22. Anna Bayfield
  23. Henrik Zetterberg
  24. Kaj Blennow
  25. Marc Suárez-Calvet
  26. Verónica Vilaplana
  27. Juan Domingo Gispert
  28. ALFA study
  29. EPAD study
  30. ADNI study
  31. OASIS study
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Abstract

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology andAPOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.

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