scholarly journals Predicting brain atrophy from tau pathology: A summary of clinical findings and their translation into personalized models

2021 ◽  
pp. 100039
Author(s):  
Amelie Schäfer ◽  
Pavanjit Chaggar ◽  
Travis B. Thompson ◽  
Alain Goriely ◽  
Ellen Kuhl
Keyword(s):  
Brain Atrophy ◽  
Clinical Findings ◽  
Tau Pathology

scholarly journals Predicting brain atrophy from tau pathology: A summary of clinical findings and their translation into personalized models

2021 ◽  
Author(s):  
Amelie Schäfer ◽  
Pavanjit Chaggar ◽  
Travis B Thompson ◽  
Alain Goriely ◽  
Ellen Kuhl
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Atrophy ◽  
Clinical Findings ◽  
Magnetic Resonance Images ◽  
Tau Pathology ◽  
Predictive Tool ◽  
Clinical Images ◽  
Atrophy Rate ◽  
Positron Emission

For more than 25 years, the amyloid hypothesis--the paradigm that amyloid is the primary cause of Alzheimer's disease--has dominated the Alzheimer's community. Now, increasing evidence suggests that tissue atrophy and cognitive decline in Alzheimer's disease are more closely linked to the amount and location of misfolded tau protein than to amyloid plaques. However, the precise correlation between tau pathology and tissue atrophy remains unknown. Here, we integrate multiphysics modeling and Bayesian inference to create personalized tau-atrophy models using longitudinal clinical images from the the Alzheimer's Disease Neuroimaging Initiative. For each subject, we infer three personalized parameters, the tau misfolding rate, the tau transport coefficient, and the tau-induced atrophy rate from four consecutive annual tau positron emission tomography scans and structural magnetic resonance images. Strikingly, the tau-induced atrophy coefficient of 0.13/year (95% CI: 0.097-0.189) was fairly consistent across all subjects suggesting a strong correlation between tau pathology and tissue atrophy. Our personalized whole brain atrophy rates of 0.68-1.68%/year (95% CI: 0.5-2.0) are elevated compared to healthy subjects and agree well with the atrophy rates of 1-3%/year reported for Alzheimer's patients in the literature. Once comprehensively calibrated with a larger set of longitudinal images, our model has the potential to serve as a diagnostic and predictive tool to estimate future atrophy progression from clinical tau images on a personalized basis.

scholarly journals de novo MAPT mutation G335A causes severe brain atrophy, 3R and 4R PHF-tau pathology and early onset frontotemporal dementia

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Kunie Ando ◽  
Lorenzo Ferlini ◽  
Valérie Suain ◽  
Zehra Yilmaz ◽  
Salwa Mansour ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Brain Atrophy ◽  
Early Onset ◽  
De Novo ◽  
Tau Pathology ◽  
Mapt Mutation

scholarly journals Frontotemporal dementia with the V337M MAPT mutation

Neurology ◽  
2017 ◽  
Vol 88 (8) ◽  
pp. 758-766 ◽  
Author(s):  
Salvatore Spina ◽  
Daniel R. Schonhaut ◽  
Bradley F. Boeve ◽  
William W. Seeley ◽  
Rik Ossenkoppele ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Mutation Carrier ◽  
Brain Atrophy ◽  
Brain Mri ◽  
Strong Association ◽  
Paired Helical Filaments ◽  
Structural Mri ◽  
Tau Pathology ◽  
Pet Tracer

Objective:To assess the efficacy of [18F]AV1451 PET in visualizing tau pathology in vivo in a patient with frontotemporal dementia (FTD) associated with the V337M microtubule-associated protein tau (MAPT) mutation.Methods:MAPT mutations are associated with the deposition of hyperphosphorylated tau protein in neurons and glia. The PET tracer [18F]AV1451 binds with high affinity to paired helical filaments tau that comprises neurofibrillary tangles in Alzheimer disease (AD), while postmortem studies suggest lower or absent binding to the tau filaments of the majority of non-AD tauopathies. We describe clinical, structural MRI, and [18F]AV1451 PET findings in a V337M MAPT mutation carrier affected by FTD and pathologic findings in his affected mother and in an unrelated V337M MAPT carrier also affected with FTD. The biochemical similarity between paired helical filament tau in AD and MAPT V337M predicts that the tau pathology associated with this mutation constitutes a compelling target for [18F]AV1451 imaging.Results:We found a strong association between topography and degree of [18F]AV1451 tracer retention in the proband and distribution of tau pathology in the brain of the proband's mother and the unrelated V337M mutation carrier. We also found a significant correlation between the degree of regional MRI brain atrophy and the extent of [18F]AV1451 binding in the proband and a strong association between the proband's clinical presentation and the extent of regional brain atrophy and tau accumulation as assessed by structural brain MRI and [18F]AV1451PET.Conclusion:Our study supports the usefulness of [18F]AV1451 to characterize tau pathology in at least a subset of pathogenic MAPT mutations.

scholarly journals Neuroinflammation predicts disease progression in progressive supranuclear palsy

2020 ◽  
Author(s):  
Maura Malpetti ◽  
Luca Passamonti ◽  
P. Simon Jones ◽  
Duncan Street ◽  
Timothy Rittman ◽  
...  
Keyword(s):  
Progressive Supranuclear Palsy ◽  
Disease Progression ◽  
Brain Atrophy ◽  
Rating Scale ◽  
Neuronal Loss ◽  
Principal Component ◽  
Rate Of Change ◽  
Tau Pathology ◽  
Clinical Progression

Objective: In addition to tau pathology and neuronal loss, neuroinflammation occurs in progressive supranuclear palsy (PSP). We test the hypotheses that baseline in vivo assessments of regional neuroinflammation ([11C]PK11195 PET), tau pathology ([18F]AV-1451 PET), and atrophy (structural MRI) predict disease progression. Methods: Seventeen patients with PSP-Richardson′s syndrome underwent a baseline multi-modal imaging assessment. Disease severity was measured at baseline and serially up to 4 years with the PSP-rating-scale (average interval 5 months). Regional grey-matter volumes and PET ligand binding potentials were summarised by three Principal Component Analyses (PCAs). A linear mixed effects model was applied to the longitudinal PSP-rating-scale scores. Single-modality imaging predictors were regressed against the individuals′ estimated rate of progression to identify the prognostic value of baseline imaging markers. Results: The PCA factors reflecting neuroinflammation and tau burden in the brainstem and cerebellum correlated with the subsequent annual rate of change in the PSP-rating-scale. PCA-derived PET markers of neuroinflammation and tau pathology correlated with brain atrophy in the same regions. However, MRI markers of brain atrophy alone did not predict clinical progression. Conclusions: Molecular imaging with PET can predict clinical progression in PSP. These data encourage the evaluation of immunomodulatory approaches to disease-modifying therapies in PSP, and the potential for PET to stratify patients for early phase clinical trials.

scholarly journals Activation of Nav1.1 ameliorates tau pathology and brain atrophy in a mouse model of tauopathy

2020 ◽  
Vol 16 (S9) ◽  
Author(s):  
Kazuaki Sampei ◽  
Chie Seki ◽  
Hiroyuki Takuwa ◽  
Jun Maeda ◽  
Maiko Ono ◽  
...  
Keyword(s):  
Mouse Model ◽  
Brain Atrophy ◽  
Tau Pathology

scholarly journals Microglial activation and tau burden predict cognitive decline in Alzheimer’s Disease

2019 ◽  
Author(s):  
Maura Malpetti ◽  
Rogier A. Kievit ◽  
Luca Passamonti ◽  
P. Simon Jones ◽  
Kamen A. Tsvetanov ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Predictive Model ◽  
Brain Atrophy ◽  
Grey Matter ◽  
Imaging Modality ◽  
Structural Mri ◽  
Tau Pathology ◽  
Single Modality

AbstractTau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer’s disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by [18F]AV-1451 PET), neuroinflammation (indexed via [11C]PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer’s disease pathology. Twenty-six patients (n=12 with clinically probable Alzheimer’s dementia and n=14 with amyloid positive Mild Cognitive Impairment) and 29 healthy controls underwent baseline assessment with [18F]AV-1451 PET, [11C]PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke’s Cognitive Examination. Regional grey-matter volumes, [18F]AV-1451 and [11C]PK11195 binding were derived from fifteen temporo-parietal regions characteristically affected by Alzheimer’s disease pathology. A Principal Component Analysis (PCA) was used on each imaging modality separately, to identify the main spatial distributions of pathology. A Latent Growth Curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals’ estimated slope of cognitive decline on the neuroimaging components and examined univariable models with single-modality predictors, and a multi-modality model of prediction, to identify the independent and combined prognostic value of the different neuroimaging markers.PCA identified a single component for the grey-matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the slope of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of [18F]AV-1451 and the first (i.e., anterior temporal) component for [11C]PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer’s disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer’s disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer’s Disease.

Clinical Findings with SAL Audiometry: Some Further Observations

1965 ◽  
Vol 30 (4) ◽  
pp. 325-335
Author(s):  
George E. Lynn ◽  
Jack A. Willeford
Keyword(s):  
Clinical Findings
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