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A model of brain function to predict tau across Alzheimer’s disease and related disorders

Nick Corriveau-Lecavalier Ge-Meng Zhang H. Botha L. Barnard H. Wiste Ellen Dicks V. Gogineni Jeyeon Lee Irene Sintini M. Senjem C. Schwarz P. Cogswell Derek R. Johnson J. Gunter Michael G. Kamykowski J. Graff-Radford V. Ramanan J. Fields M. Machulda N. Stricker Brad F. Boeve V. Lowe K. Kantarci D. Knopman Jennifer L. Whitwell K. Josephs Ronald C. Petersen C. Jack David T. Jones

Oct 2026 · Nature Communications · 0 citations

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 ( R 2 ~ 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.

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