A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE.

ABSTRACT: Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make infere

SUBMITTER: Le TT 

PROVIDER: S-EPMC6208001 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature