Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Neuroimage. 2022 Aug 1:256:119228. doi: 10.1016/j.neuroimage.2022.119228. Epub 2022 Apr 20.


"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

Keywords: Alzheimer disease; Brain aging; Machine learning; Resting-state functional connectivity; fMRI.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Alzheimer Disease* / pathology
  • Biomarkers
  • Brain / physiology
  • Humans
  • Magnetic Resonance Imaging / methods
  • Middle Aged
  • Neuroimaging
  • Young Adult


  • Biomarkers