Alpha‐frequency synchronization deficits during life predict postmortem neurofibrillary tangle burden in Alzheimer's disease: Biomarkers: Leveraging postmortem collections to validate neuroimaging. (7th December 2020)

• Record Type: Journal Article
• Authors: Ranasinghe, Kamalini G; Petersen, Cathrine; Kudo, Kiwamu; Srivatsan, Srivatsan; Beagle, Alexander J; Mizuiri, Danielle; Findlay, Anne; Houde, John F; Rankin, Katherine; Rabinovici, Gil D; Seeley, William W.; Spina, Salvatore; Gorno‐Tempini, Marilu; Kramer, Joel H; Miller, Bruce L; Vossel, Keith A; Grinberg, Lea Tenenholz; Nagarajan, Srikantan S
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.045351
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Using a multimodal imaging approach with neurophysiological assessments incorporated alongside molecular markers in patients with AD, we have previously demonstrated frequency‐specific neural synchronization deficits distinctly associated with tau and Aβ tracer uptake. Reduced alpha (8‐12 Hz) synchrony (alpha hyposynchrony) closely mapped onto regional patterns of tau uptake and was modulated by the degree of tau uptake. In contrast, increased delta‐theta (2‐8 Hz) synchrony (delta‐theta hypersynchrony) showed strong associations with Aβ tracer uptake. Here, we examined the associations between regional neurofibrillary tangle (NFT) pathology and frequency‐specific neuronal synchrony in patients with AD. Methods: In a well‐characterized clinicopathological cohort of AD (n=13; Figure 1), we quantified the postmortem NFT density using thioflavin‐S fluorescent microscopy within six selected neocortical and hippocampal regions including, angular gyrus, superior temporal gurus, middle frontal gyrus, primary motor cortex, CA1 and subiculum. In the same patients, using resting‐state magnetoencephalography (MEG) we quantified the degree of alpha and delta‐theta neural synchrony abnormalities, compared to an age‐matched control group (n=23). We used linear mixed effects statistical models to investigate the associations between frequency‐specific neurophysiological indices and NFT burden. The duration between MEG scan and death, Clinical Dementia Rating (CDR) at death, and the difference of CDR‐Sum‐of‐Boxes between MEG scan and death were included into the mixed models as additional variables, together with the subject identity as a repeated factor. Results: We found that alpha hyposynchrony negatively predicted the NFT burden in patients with AD, in contrast, delta‐theta hypersynchrony did not show significant associations with the NFT burden (b=‐7.42, F=10.17, P=0.01 for alpha‐hyposynchrony; b=0.73, F=0.07, P=0.8 for delta‐theta hypersynchrony; Figure 2). Results were similar with a restricted analysis on neocortical regions after exclusion of subcortical regions (b=‐13.13, F=8.75, P=0.02 for alpha‐hyposynchrony; b=‐1.79, F=0.00, P=0.97 for delta‐theta hypersynchrony). Conclusions: The current study characterizes the frequency‐specific nature of neurophysiological signatures in AD pathophysiology and demonstrates that alpha hyposynchrony is a sensitive index of network disruptions mediated by abnormally phosphorylated tau proteins. These findings suggest that neurophysiological indices may be useful biomarkers to detect earliest manifestations of dysfunctional neural networks as well as to gauge therapeutic efficacy in clinical trials.