Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease.

ABSTRACT: Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans. Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer's disease (AD). We showed that amyloid status is a critical feature for brain age prediction models. We trained a model on T1-weighted MRI images participants without amyloid pathology. MRI data were processed to estimate gray matter density voxel-wise, which were the

SUBMITTER: Ly M 

PROVIDER: S-EPMC7064421 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature