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

for the Dominantly Inherited Alzheimer Network, Peter R. Millar, Patrick H. Luckett, Brian A. Gordon, Tammie L.S. Benzinger, Suzanne E. Schindler, Anne M. Fagan, Carlos Cruchaga, Randall J. Bateman, John C. Morris, Beau M. Ances, Nicolas Barthelemy, Randall Bateman, Gregory S. Day, Nelly Joseph-Mathurin, Jason Hassenstab, David Holtzman, Celeste Karch, Yan Li, Eric McDadeJohn Morris, Richard Perrin, Qing Wang, Chengjie Xiong, Jinbin Xu

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

“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.

Original languageEnglish
Article number119228
JournalNeuroImage
Volume256
DOIs
StatePublished - Aug 1 2022

Keywords

  • Alzheimer disease
  • Brain aging
  • Machine learning
  • Resting-state functional connectivity
  • fMRI

Fingerprint

Dive into the research topics of 'Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease'. Together they form a unique fingerprint.

Cite this