Artificial neural networks for non-linear age correction of diffusion metrics in the brain.

ABSTRACT: Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network

SUBMITTER: Kocar TD 

PROVIDER: S-EPMC9632350 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature