A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

ABSTRACT: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best mo

SUBMITTER: Karlsson L 

PROVIDER: S-EPMC11160861 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature