Millar, Peter R.Luckett, Patrick H.Gordon, Brian A.Benzinger, Tammie L. S.Schindler, Suzanne E.Fagan, Anne M.Cruchaga, CarlosBateman, Randall J.Allegri, RicardoJucker, MathiasLee, Jae-HongMori, HiroshiSalloway, Stephen P.Yakushev, IgorDominantly Inherited Alzheimer NetworkMorris, John C.Ances, Beau M.2025-03-112025-03-112022Millar PR, Luckett PH, Gordon BA, et al. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. Neuroimage. 2022;256:119228. doi:10.1016/j.neuroimage.2022.119228https://hdl.handle.net/1805/46292"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.en-USPublisher PolicyAlzheimer diseaseBrain agingMachine learningResting-state functional connectivityfMRIArticle