A model of brain function to predict tau across Alzheimer’s disease and related disorders
TL;DR
A machine learning model using FDG-PET imaging to accurately predict Alzheimer’s-type tau pathology across spectrums of aging and dementia is developed, providing a novel framework to estimate tau burden non-invasively.
Abstract
We developed a machine learning framework grounded in a computational model of brain function and FDG-PET imaging to predict Alzheimer’s disease (AD)-type tau across AD and related disorders (ADRD). The model was trained on 1088 research participants spanning normal aging and ADRD with FDG-PET and flortaucipir-PET (FTP-PET). Validation was conducted in: (1) a longitudinal subset of the training cohort (n = 215), (2) Alzheimer’s Disease Neuroimaging Initiative (n = 295), and (3) a heretogeneous clinical cohort (n = 264; n = 28 with FTP-PET). Results showed that the model accurately predicted FTP-PET when applied at the participant-level (R2 ~ 0.34-0.71). It performed well in clinical scenarios, including classification of low versus high tau stages (79–95% accuracy) and prediction of tau in a heterogeneous clinical cohort without FTP-PET. These findings advance the understanding of the relationship between neurodegeneration and AD-type tau and provide a framework for estimating AD-type tau across ADRD. Researchers developed a machine learning model using FDG-PET imaging to accurately predict Alzheimer’s-type tau pathology across spectrums of aging and dementia, providing a novel framework to estimate tau burden non-invasively.