Deep learning-based brain age prediction in normal aging and dementia

Jeyeon Lee, Brian J. Burkett, Hoon Ki Min, Matthew L. Senjem, Emily S. Lundt, Hugo Botha, Jonathan Graff-Radford, Leland R. Barnard, Jeffrey L. Gunter, Christopher Schwarz, Kejal Kantarci, David S Knopman, Bradley F. Boeve, Val J. Lowe, Ronald C. Petersen, Clifford R Jr. Jack, David T Jones

Research output: Contribution to journalArticlepeer-review

Abstract

Brain aging is accompanied by patterns of functional and structural change. Alzheimer’s disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.

Original languageEnglish (US)
Pages (from-to)412-424
Number of pages13
JournalNature Aging
Volume2
Issue number5
DOIs
StatePublished - May 2022

ASJC Scopus subject areas

  • Aging
  • Geriatrics and Gerontology
  • Neuroscience (miscellaneous)

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