Abstract
INTRODUCTION
Regional tau is strongly associated with Alzheimer’s disease (AD) symptoms, but tau positron emission tomography (PET) is not routinely available, limiting early risk stratification.
METHODS
We trained a deep learning model to predict regional tau PET standardized uptake value ratios (SUVRs) from magnetic resonance imaging (MRI) with and without basic demographics using public cohorts (Alzheimer’s Disease Neuroimaging Initiative [ADNI], Open Access Series of Imaging Studies 3 [OASIS3-tau], Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease Study [A4], Harvard Aging Brain Study [HABS]). Mean absolute error (MAE), Pearson r, area-under-the-receiver-operating-curve (AUC), and comparison with cognitive testing were assessed in external test sets (SCAN, Stanford University).
RESULTS
The model achieved an MAE of 0.14 (95% confidence interval [CI]: 0.12–0.16) and r = 0.72 for an early uptake region (Braak I-IV), and an MAE of 0.14 (95% CI: 0.12–0.17) and r = 0.71 for a late uptake region (Braak V-VI). AUCs for binary tau positivity in these regions were 0.87 (95% CI: 0.83–0.90) and 0.88 (95% CI: 0.84–0.91), respectively.
DISCUSSION
Predicting regional tau burden from magnetic resonance imaging (MRI) is feasible and may enable scalable tau-informed clinical AD care.
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This post is Copyright: | October 30, 2026
Neuro-Dementia