scholarly journals Patient-centered connectivity-based prediction of tau pathology spread in Alzheimer’s disease

2020 ◽  
Vol 6 (48) ◽  
pp. eabd1327
Author(s):  
Nicolai Franzmeier ◽  
Anna Dewenter ◽  
Lukas Frontzkowski ◽  
Martin Dichgans ◽  
Anna Rubinski ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Patient Specific ◽  
Braak Stage ◽  
Accumulation Rates ◽  
Patient Centered ◽  
Braak Staging ◽  
Two Samples ◽  
Tau Pet

In Alzheimer’s disease (AD), the Braak staging scheme suggests a stereotypical tau spreading pattern that does, however, not capture interindividual variability in tau deposition. This complicates the prediction of tau spreading, which may become critical for defining individualized tau-PET readouts in clinical trials. Since tau is assumed to spread throughout connected regions, we used functional connectivity to improve tau spreading predictions over Braak staging methods. We included two samples with longitudinal tau-PET from controls and AD patients. Cross-sectionally, we found connectivity of tau epicenters (i.e., regions with earliest tau) to predict estimated tau spreading sequences. Longitudinally, we found tau accumulation rates to correlate with connectivity strength to patient-specific tau epicenters. A connectivity-based, patient-centered tau spreading model improved the assessment of tau accumulation rates compared to Braak stage–specific readouts and reduced sample sizes by ~40% in simulated tau-targeting interventions. Thus, connectivity-based tau spreading models may show utility in clinical trials.

scholarly journals Tau-PET and in vivo Braak-staging as a prognostic marker in Alzheimer’s disease

2021 ◽  
Author(s):  
Davina Biel ◽  
Matthias Brendel ◽  
Anna Rubinski ◽  
Katharina Buerger ◽  
Daniel Janowitz ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Prognostic Marker ◽  
Cognitive Change ◽  
Patient Specific ◽  
Amyloid Pet ◽  
Braak Stage ◽  
Braak Staging ◽  
Tau Pet

ABSTRACTINTRODUCTIONTau pathology in Alzheimer’s disease tracks clinical status more closely than beta-amyloid. Thus, tau-PET may be a promising prognostic marker for cognitive decline. Here, we systematically compared tau-PET and Braak-staging vs. amyloid-PET as predictors of cognitive decline.METHODSWe included 396 cognitively normal to dementia subjects with 18F-Flutemetamol/18F-Florbetapir-amyloid-PET, 18F-Flortaucipir-tau-PET and ~2-year cognitive assessments. Annual cognitive change rates were calculated via linear-mixed models. We determined global amyloid-PET, global tau-PET, and tau-PET-based Braak-stage (Braak0/BraakI+/BraakI-IV+/BraakI-VI+/Braakatypical+). In bootstrapped linear regression, we assessed whether tau-PET outperformed amyloid-PET in predicting cognitive decline. Using ANCOVAs, we tested whether later Braak-stage predicted accelerated cognitive decline and determined Braak-stage-specific conversion risk to MCI or dementia.RESULTSGlobal tau-PET was a better predictor of cognitive decline than global amyloid-PET (p<0.001). Advanced Braak-stage was associated with faster cognitive decline (p<0.001) and elevated clinical conversion risk.DISCUSSIONTau-PET and Braak-staging show promise for predicting patient-specific risk of clinical AD progression.

scholarly journals 18F-MK-6240 PET for early and late detection of neurofibrillary tangles

Brain ◽  
2020 ◽  
Vol 143 (9) ◽  
pp. 2818-2830 ◽  
Author(s):  
Tharick A Pascoal ◽  
Joseph Therriault ◽  
Andrea L Benedet ◽  
Melissa Savard ◽  
Firoza Z Lussier ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amyloid Β ◽  
Standardized Uptake Value ◽  
Braak Staging ◽  
High Affinity ◽  
Tau Pet ◽  
Transentorhinal Cortex

Abstract Braak stages of tau neurofibrillary tangle accumulation have been incorporated in the criteria for the neuropathological diagnosis of Alzheimer’s disease. It is expected that Braak staging using brain imaging can stratify living individuals according to their individual patterns of tau deposition, which may prove crucial for clinical trials and practice. However, previous studies using the first-generation tau PET agents have shown a low sensitivity to detect tau pathology in areas corresponding to early Braak histopathological stages (∼20% of cognitively unimpaired elderly with tau deposition in regions corresponding to Braak I–II), in contrast to ∼80–90% reported in post-mortem cohorts. Here, we tested whether the novel high affinity tau tangles tracer 18F-MK-6240 can better identify individuals in the early stages of tau accumulation. To this end, we studied 301 individuals (30 cognitively unimpaired young, 138 cognitively unimpaired elderly, 67 with mild cognitive impairment, 54 with Alzheimer’s disease dementia, and 12 with frontotemporal dementia) with amyloid-β 18F-NAV4694, tau 18F-MK-6240, MRI, and clinical assessments. 18F-MK-6240 standardized uptake value ratio images were acquired at 90–110 min after the tracer injection. 18F-MK-6240 discriminated Alzheimer’s disease dementia from mild cognitive impairment and frontotemporal dementia with high accuracy (∼85–100%). 18F-MK-6240 recapitulated topographical patterns consistent with the six hierarchical stages proposed by Braak in 98% of our population. Cognition and amyloid-β status explained most of the Braak stages variance (P &lt; 0.0001, R2 = 0.75). No single region of interest standardized uptake value ratio accurately segregated individuals into the six topographic Braak stages. Sixty-eight per cent of the cognitively unimpaired elderly amyloid-β-positive and 37% of the cognitively unimpaired elderly amyloid-β-negative subjects displayed tau deposition, at least in the transentorhinal cortex (Braak I). Tau deposition solely in the transentorhinal cortex was associated with an elevated prevalence of amyloid-β, neurodegeneration, and cognitive impairment (P &lt; 0.0001). 18F-MK-6240 deposition in regions corresponding to Braak IV–VI was associated with the highest prevalence of neurodegeneration, whereas in Braak V–VI regions with the highest prevalence of cognitive impairment. Our results suggest that the hierarchical six-stage Braak model using 18F-MK-6240 imaging provides an index of early and late tau accumulation as well as disease stage in preclinical and symptomatic individuals. Tau PET Braak staging using high affinity tracers has the potential to be incorporated in the diagnosis of living patients with Alzheimer’s disease in the near future.

scholarly journals In vivo Braak‐staging using 18 F‐Flortaucipir‐tau‐PET as a predictive marker for future cognitive decline in Alzheimer’s disease

2021 ◽  
Vol 17 (S1) ◽  
Author(s):  
Davina Biel ◽  
Matthias Brendel ◽  
Anna Rubinski ◽  
Katharina Buerger ◽  
Daniel Janowitz ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Predictive Marker ◽  
Braak Staging ◽  
Tau Pet

scholarly journals Characterization of Alzheimer’s tau biomarker discordance using plasma, CSF, and PET

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yu Guo ◽  
◽  
Yu-Yuan Huang ◽  
Xue-Ning Shen ◽  
Shi-Dong Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Amyloid Β ◽  
Regions Of Interest ◽  
Emission Tomography ◽  
Braak Stage ◽  
Positron Emission ◽  
Tau Pet

Abstract Background We aimed to investigate the tau biomarker discrepancies of Alzheimer’s disease (AD) using plasma tau phosphorylated at threonine 181 (p-tau181), cerebrospinal fluid (CSF) p-tau181, and AV1451 positron emission tomography (PET). Methods In the Alzheimer’s Disease Neuroimaging Initiative, 724 non-demented participants were categorized into plasma/CSF and plasma/PET groups. Demographic and clinical variables, amyloid-β (Aβ) burden, flortaucipir-PET binding in Braak regions of interest (ROIs), longitudinal changes in clinical outcomes, and conversion risk were compared. Results Across different tau biomarker groups, the proportion of participants with a discordant profile varied (plasma+/CSF− 15.6%, plasma−/CSF+ 15.3%, plasma+/PET− 22.4%, and plasma−/PET+ 6.1%). Within the plasma/CSF categories, we found an increase from concordant-negative to discordant to concordant-positive in the frequency of Aβ pathology or cognitive impairment, rates of cognitive decline, and risk of cognitive conversion. However, the two discordant categories (plasma+/CSF− and plasma−/CSF+) showed comparable performances, resulting in similarly reduced cognitive capacities. Regarding plasma/PET categories, as expected, PET-positive individuals had increased Aβ burden, elevated flortaucipir retention in Braak ROIs, and accelerated cognitive deterioration than concordant-negative persons. Noteworthy, discordant participants with normal PET exhibited reduced flortaucipir uptake in Braak stage ROIs and slower rates of cognitive decline, relative to those PET-positive. Therefore, individuals with PET abnormality appeared to have advanced tau pathological changes and poorer cognitive function, regardless of the plasma status. Furthermore, these results were found only in individuals with Aβ pathology. Conclusions Our results indicate that plasma and CSF p-tau181 abnormalities associated with amyloidosis occur simultaneously in the progression of AD pathogenesis and related cognitive decline, before tau-PET turns positive.

scholarly journals Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Taeho Jo ◽  
◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Andrew J. Saykin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Deep Learning ◽  
Early Detection ◽  
Medial Temporal Lobe ◽  
Memory Loss ◽  
Disease Classification ◽  
Classification Model ◽  
Tau Pet

Abstract Background Alzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. Results The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI). Conclusion A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

scholarly journals Alzheimer’s disease clinical variants show distinct regional patterns of neurofibrillary tangle accumulation

2019 ◽  
Author(s):  
Cathrine Petersen ◽  
Amber L Nolan ◽  
Elisa de Paula França Resende ◽  
Alexander Ehrenberg ◽  
Salvatore Spina ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hierarchical Clustering ◽  
Primary Motor Cortex ◽  
Regional Distribution ◽  
Neurofibrillary Tangle ◽  
Angular Gyrus ◽  
Braak Stage ◽  
Regional Patterns ◽  
Braak Staging

ABSTRACTBackgroundNeurofibrillary tangle (NFT) pathology in Alzheimer’s disease (AD) follows a stereotypic progression well-characterized by Braak staging. However, some AD cases show deviations from the Braak staging scheme. In this study, we tested the hypothesis that these variations in the regional distribution of tau pathology are linked to heterogeneity in the clinical phenotypes of AD.MethodsWe included a clinicopathological cohort of ninety-four AD cases enriched for atypical clinical presentations. Subjects underwent apolipoprotein E (APOE) genotyping and neuropsychological testing. Main cognitive domains (executive, visuospatial, language, and memory function) were assessed using an established composite z-score. We assessed NFT density and distribution from thioflavin S fluorescent microscopy throughout four neocortical and two hippocampal regions. A mathematical algorithm classifying AD cases into typical, hippocampal sparing (HpSp), and limbic predominant (LP) subtypes based on regional NFT burden was compared to unbiased hierarchical clustering for cases with Braak stage > IV.ResultsPatients diagnosed with logopenic primary progressive aphasia showed significantly higher NFT density in the superior temporal gyrus relative to patients diagnosed with Alzheimer-type dementia (p = 0.0091), while patients with corticobasal syndrome showed significantly higher NFT density in the primary motor cortex (p = 0.0205). Hierarchical clustering identified three discrete clusters of patients characterized respectively by low overall NFT burden (n = 18), high overall burden (n = 30), and cortical-predominant burden (n = 24). A regionally specific effect was observed for visuospatial ability; higher NFT density in the angular gyrus (β = - 0.0921, p = 0.0099) and in the CA1 sector of the hippocampus (β = −0.0735, p = 0.0380) was significantly associated with more severe visuospatial dysfunction, modulated by age of death.ConclusionsOur results suggest domain-specific functional consequences of regional NFT accumulation. In particular, we observed focal aggregation of NFT density in clinically relevant regions among different clinical AD variants. Continued work to map the regionally specific clinical consequences of tau accumulation presents an opportunity to increase understanding of disease mechanisms underlying atypical clinical manifestations.

scholarly journals Sample size estimation for clinical trials with tau‐PET SUVR as the primary outcome in dominantly inherited Alzheimer’s disease

2021 ◽  
Vol 17 (S9) ◽  
Author(s):  
Guoqiao Wang ◽  
Yan Li ◽  
Chengjie Xiong ◽  
Tammie L.S. Benzinger ◽  
Brian A. Gordon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Sample Size ◽  
Primary Outcome ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Tau Pet

scholarly journals Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

2020 ◽  
Author(s):  
Taeho Jo ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Andrew J. Saykin ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Deep Learning ◽  
Early Detection ◽  
Group Analysis ◽  
Statistical Parametric Mapping ◽  
Classification Model ◽  
Tau Pet ◽  
3D Cnn

AbstractBackgroundAlzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans.MethodsWe analysed [18F]flortaucipir PET image data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. We first developed an image classifier to distinguish AD from cognitively normal (CN) older adults by training a 3D convolutional neural network (CNN)-based deep learning model on tau PET images (N=132; 66 CN and 66 AD), then applied the classifier to images from individuals with mild cognitive impairment (MCI; N=168). In addition, we applied a layer-wise relevance propagation (LRP)-based model to identify informative features and to visualize classification results. We compared these results with those from whole brain voxel-wise between-group analysis using conventional Statistical Parametric Mapping (SPM12).ResultsThe 3D CNN-based classification model of AD from CN yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The LRP results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r=0.43 for early MCI and r=0.49 for late MCI).ConclusionA deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

scholarly journals Tau-PET and in vivo Braak-staging as prognostic markers of future cognitive decline in cognitively normal to demented individuals

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Davina Biel ◽  
Matthias Brendel ◽  
Anna Rubinski ◽  
Katharina Buerger ◽  
Daniel Janowitz ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Prognostic Markers ◽  
Patient Specific ◽  
Amyloid Pet ◽  
Braak Stage ◽  
Cognitively Normal ◽  
Braak Staging ◽  
Pet Tracer ◽  
Tau Pet

Abstract Background To systematically examine the clinical utility of tau-PET and Braak-staging as prognostic markers of future cognitive decline in older adults with and without cognitive impairment. Methods In this longitudinal study, we included 396 cognitively normal to dementia subjects with 18F-Florbetapir/18F-Florbetaben-amyloid-PET, 18F-Flortaucipir-tau-PET and ~ 2-year cognitive follow-up. Annual change rates in global cognition (i.e., MMSE, ADAS13) and episodic memory were calculated via linear-mixed models. We determined global amyloid-PET (Centiloid) plus global and Braak-stage-specific tau-PET SUVRs, which were stratified as positive(+)/negative(−) at pre-established cut-offs, classifying subjects as Braak0/BraakI+/BraakI–IV+/BraakI–VI+/Braakatypical+. In bootstrapped linear regression, we assessed the predictive accuracy of global tau-PET SUVRs vs. Centiloid on subsequent cognitive decline. To test for independent tau vs. amyloid effects, analyses were further controlled for the contrary PET-tracer. Using ANCOVAs, we tested whether more advanced Braak-stage predicted accelerated future cognitive decline. All models were controlled for age, sex, education, diagnosis, and baseline cognition. Lastly, we determined Braak-stage-specific conversion risk to mild cognitive impairment (MCI) or dementia. Results Baseline global tau-PET SUVRs explained more variance (partial R2) in future cognitive decline than Centiloid across all cognitive tests (Cohen’s d ~ 2, all tests p < 0.001) and diagnostic groups. Associations between tau-PET and cognitive decline remained consistent when controlling for Centiloid, while associations between amyloid-PET and cognitive decline were non-significant when controlling for tau-PET. More advanced Braak-stage was associated with gradually worsening future cognitive decline, independent of Centiloid or diagnostic group (p < 0.001), and elevated conversion risk to MCI/dementia. Conclusion Tau-PET and Braak-staging are highly predictive markers of future cognitive decline and may be promising single-modality estimates for prognostication of patient-specific progression risk in clinical settings.

scholarly journals Is longitudinal tau PET ready for use in Alzheimer’s disease clinical trials?

Brain ◽  
2018 ◽  
Vol 141 (5) ◽  
pp. 1241-1244 ◽  
Author(s):  
Oskar Hansson ◽  
Elizabeth C Mormino
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Tau Pet
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