EBioMedicine · 2026
Differential associations of tau extent and load with brain metabolic and cognitive dysfunction in Alzheimer's disease.
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Macedo Arthur C, Trudel Lydia, Hosseini Seyyed Ali, Bezgin Gleb, Quispialaya-Socualaya Kely, Therriault Joseph, Rahmouni Nesrine, Tissot Cécile, Woo Marcel S, Limberger Christian, Machado Luiza S, Souza Débora G, Oliva-Lopez Delphine, Mitchell Stuart, Chan Tevy, Hall Brandon, Aumont Étienne, Servaes Stijn, Gonçalves Marina Pereira, Bernardes Real Ana Paula, Klostranec Jesse, Vitali Paolo, Provost Karine, Pascoal Tharick A, Zimmer Eduardo R, Soucy Jean-Paul, Rosa-Neto Pedro, Alzheimer's Disease Neuroimaging Initiative
Аннотация
BACKGROUND: Reduced [18F]Fluorodeoxyglucose ([18F]FDG)-PET uptake is a core imaging feature of Alzheimer's disease (AD). While tau load correlates with this metabolic signature, it remains unclear whether the spatial extent of tauopathy (SEOT) more accurately explains brain glucose hypometabolic patterns. Here, we compared SEOT versus tau load to determine their ability to predict brain hypometabolic signatures in AD.
METHODS: We performed a cross-sectional study of amyloid-β positive participants from ADNI (n = 150) and an atypical AD subset from the McGill University Research Centre for Studies in Ageing (MCSA; n = 44). Participants underwent [18F]AV1451 or [18F]MK6240 tau-PET and [18F]FDG-PET. Tau load was indexed with regional SUVR, and SEOT with the proportion of abnormal voxels. Linear regressions related temporal and whole-cortex tau-PET load or SEOT to [18F]FDG-PET. We also compared the accuracy of tau-PET metrics for identifying AD-like hypometabolism. Spearman correlations assessed SEOT/tau load-FDG associations at regional and network levels. Partial Least Squares (PLS) regression investigated whether distributed tau load and SEOT predicted [18F]FDG-PET signatures. Structural equation modelling and hierarchical linear models assessed associations between tau metrics and cognition dependent and independent of [18F]FDG-PET.
FINDINGS: Whole-cortex SEOT best predicted decreased signal in the [18F]FDG-PET AD-meta-ROI. SEOT also performed better in classifying AD-related brain hypometabolism. Across regions and networks, SEOT performed similarly or better than tau load in predicting metabolic dysfunction. Voxelwise analyses suggested complementary predictive value of SEOT and tau load, each capturing slightly distinct spatial associations with [18F]FDG-PET. PLS demonstrated partially non-redundant contributions from tau load and SEOT. Cortical SEOT showed the strongest predictive value for cognition.
INTERPRETATION: SEOT provides complementary, independent, and often stronger predictive value than tau load for brain metabolism, particularly for network-level dysfunction. SEOT may improve diagnostic characterisation and prediction of cognitive impairment beyond [18F]FDG-PET.
FUNDING: TRIAD is supported by the Weston Brain Institute, Canadian Institutes of Health Research, Canadian Consortium of Neurodegeneration and Aging, Brain Canada Foundation, the Fonds de Recherche du Québec - Santé, and the Colin J Adair Charitable Foundation. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and the Canadian Institutes of Health Research.
METHODS: We performed a cross-sectional study of amyloid-β positive participants from ADNI (n = 150) and an atypical AD subset from the McGill University Research Centre for Studies in Ageing (MCSA; n = 44). Participants underwent [18F]AV1451 or [18F]MK6240 tau-PET and [18F]FDG-PET. Tau load was indexed with regional SUVR, and SEOT with the proportion of abnormal voxels. Linear regressions related temporal and whole-cortex tau-PET load or SEOT to [18F]FDG-PET. We also compared the accuracy of tau-PET metrics for identifying AD-like hypometabolism. Spearman correlations assessed SEOT/tau load-FDG associations at regional and network levels. Partial Least Squares (PLS) regression investigated whether distributed tau load and SEOT predicted [18F]FDG-PET signatures. Structural equation modelling and hierarchical linear models assessed associations between tau metrics and cognition dependent and independent of [18F]FDG-PET.
FINDINGS: Whole-cortex SEOT best predicted decreased signal in the [18F]FDG-PET AD-meta-ROI. SEOT also performed better in classifying AD-related brain hypometabolism. Across regions and networks, SEOT performed similarly or better than tau load in predicting metabolic dysfunction. Voxelwise analyses suggested complementary predictive value of SEOT and tau load, each capturing slightly distinct spatial associations with [18F]FDG-PET. PLS demonstrated partially non-redundant contributions from tau load and SEOT. Cortical SEOT showed the strongest predictive value for cognition.
INTERPRETATION: SEOT provides complementary, independent, and often stronger predictive value than tau load for brain metabolism, particularly for network-level dysfunction. SEOT may improve diagnostic characterisation and prediction of cognitive impairment beyond [18F]FDG-PET.
FUNDING: TRIAD is supported by the Weston Brain Institute, Canadian Institutes of Health Research, Canadian Consortium of Neurodegeneration and Aging, Brain Canada Foundation, the Fonds de Recherche du Québec - Santé, and the Colin J Adair Charitable Foundation. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and the Canadian Institutes of Health Research.