Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias.

ABSTRACT: Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area,

SUBMITTER: Jirsaraie RJ 

PROVIDER: S-EPMC9875922 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature