Merle C Hoenig1,2, Gérard N Bischof3,4, Özgür A Onur5,6, Juraj Kukolja7, Frank Jessen8,9, Klaus Fliessbach9,10, Bernd Neumaier11,12, Gereon R Fink5,6, Elke Kalbe13, Alexander Drzezga3,14,9, Thilo van Eimeren3,5,9. 1. Multimodal Neuroimaging, Department of Nuclear Medicine, Medical Faculty and University Hospital, University Hospital Cologne, Cologne, Germany. merle.hoenig@uk-koeln.de. 2. Molecular Organization of the Brain, Institute of Neuroscience and Medicine (INM-2), Research Center Jülich, Jülich, Germany. merle.hoenig@uk-koeln.de. 3. Multimodal Neuroimaging, Department of Nuclear Medicine, Medical Faculty and University Hospital, University Hospital Cologne, Cologne, Germany. 4. Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany. 5. Department of Neurology, Medical Faculty and University Hospital, University of Cologne, Cologne, Germany. 6. Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany. 7. Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, Wuppertal, Germany. 8. Department of Psychiatry, Medical Faculty and University Hospital, University of Cologne, Cologne, Germany. 9. German Center for Neurodegenerative Diseases (DZNE), Bonn/Cologne, Germany. 10. Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany. 11. Nuclear Chemistry, Institute of Neuroscience and Medicine (INM-5), Research Center Jülich, Jülich, Germany. 12. Institute of Radiochemistry and Experimental Molecular Imaging, Medical Faculty and University Hospital, University of Cologne, Cologne, Germany. 13. Department of Medical Psychology | Neuropsychology and Gender Studies & Center for Neuropsychological Diagnostics and Intervention (CeNDI), Medical Faculty and University Hospital, University of Cologne, Cologne, Germany. 14. Molecular Organization of the Brain, Institute of Neuroscience and Medicine (INM-2), Research Center Jülich, Jülich, Germany.
Abstract
PURPOSE: Using PET imaging in a group of patients with Alzheimer's disease (AD), we investigated whether level of education, a proxy for resilience, mitigates the harmful impact of tau pathology on neuronal function. METHODS: We included 38 patients with mild-to-moderate AD (mean age 67 ± 7 years, mean MMSE score 24 ± 4, mean years of education 14 ± 4; 20 men, 18 women) in whom a [18F]AV-1451 scan (a measure of tau pathology) and an [18F]FDG scan (a measure of neuronal function) were available. The preprocessed PET scans were z-transformed using templates for [18F]AV-1451 and [18F]FDG from healthy controls, and subsequently thresholded at a z-score of ≥3.0, representing an one-tailed p value of 0.001. Next, three volumes were computed in each patient: the tau-specific volume (tau pathology without neuronal dysfunction), the FDG-specific volume (neuronal dysfunction without tau pathology), and the overlap volume (tau pathology and neuronal dysfunction). Mean z-scores and volumes were extracted and used as dependent variables in regression analysis with years of education as predictor, and age and MMSE score as covariates. RESULTS: Years of education were positively associated with tau-specific volume (β = 0.362, p = 0.022), suggesting a lower impact of tau pathology on neuronal function in patients with higher levels of education. Concomitantly, level of education was positively related to tau burden in the overlap volume (β = 0.303, p = 0.036) implying that with higher levels of education more tau pathology is necessary to induce neuronal dysfunction. CONCLUSION: In patients with higher levels of education, tau pathology is less paralleled by regional and remote neuronal dysfunction. The data suggest that early life-time factors such as level of education support resilience mechanisms, which ameliorate AD-related effects later in life.
PURPOSE: Using PET imaging in a group of patients with Alzheimer's disease (AD), we investigated whether level of education, a proxy for resilience, mitigates the harmful impact of tau pathology on neuronal function. METHODS: We included 38 patients with mild-to-moderate AD (mean age 67 ± 7 years, mean MMSE score 24 ± 4, mean years of education 14 ± 4; 20 men, 18 women) in whom a [18F]AV-1451 scan (a measure of tau pathology) and an [18F]FDG scan (a measure of neuronal function) were available. The preprocessed PET scans were z-transformed using templates for [18F]AV-1451 and [18F]FDG from healthy controls, and subsequently thresholded at a z-score of ≥3.0, representing an one-tailed p value of 0.001. Next, three volumes were computed in each patient: the tau-specific volume (tau pathology without neuronal dysfunction), the FDG-specific volume (neuronal dysfunction without tau pathology), and the overlap volume (tau pathology and neuronal dysfunction). Mean z-scores and volumes were extracted and used as dependent variables in regression analysis with years of education as predictor, and age and MMSE score as covariates. RESULTS: Years of education were positively associated with tau-specific volume (β = 0.362, p = 0.022), suggesting a lower impact of tau pathology on neuronal function in patients with higher levels of education. Concomitantly, level of education was positively related to tau burden in the overlap volume (β = 0.303, p = 0.036) implying that with higher levels of education more tau pathology is necessary to induce neuronal dysfunction. CONCLUSION: In patients with higher levels of education, tau pathology is less paralleled by regional and remote neuronal dysfunction. The data suggest that early life-time factors such as level of education support resilience mechanisms, which ameliorate AD-related effects later in life.
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