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

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 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 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.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
Author(s):  
Nishant Verma ◽  
S. Natasha Beretvas ◽  
Belen Pascual ◽  
Joseph C. Masdeu ◽  
Mia K. Markey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Latent Variable ◽  
Brain Regions ◽  
Disease Assessment ◽  
Latent Variable Analysis ◽  
The Relationship ◽  
Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

Recent Updates in the Alzheimer’s Disease Etiopathology and Possible Treatment Approaches: A Narrative Review of Current Clinical Trials

2020 ◽  
Vol 13 (4) ◽  
pp. 273-294 ◽  
Author(s):  
Elahe Zarini-Gakiye ◽  
Javad Amini ◽  
Nima Sanadgol ◽  
Gholamhassan Vaezi ◽  
Kazem Parivar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Small Molecules ◽  
Clinical Trial Design ◽  
Treatment Approaches ◽  
Drug Candidates ◽  
Novel Treatments ◽  
Approved Drugs ◽  
Tau Aggregation

Background: Alzheimer’s disease (AD) is the most frequent subtype of incurable neurodegenerative dementias and its etiopathology is still not clearly elucidated. Objective: Outline the ongoing clinical trials (CTs) in the field of AD, in order to find novel master regulators. Methods: We strictly reviewed all scientific reports from Clinicaltrials.gov and PubMed databases from January 2010 to January 2019. The search terms were “Alzheimer's disease” or “dementia” and “medicine” or “drug” or “treatment” and “clinical trials” and “interventions”. Manuscripts that met the objective of this study were included for further evaluations. Results: Drug candidates have been categorized into two main groups including antibodies, peptides or hormones (such as Ponezumab, Interferon β-1a, Solanezumab, Filgrastim, Levemir, Apidra, and Estrogen), and naturally-derived ingredients or small molecules (such as Paracetamol, Ginkgo, Escitalopram, Simvastatin, Cilostazo, and Ritalin-SR). The majority of natural candidates acted as anti-inflammatory or/and anti-oxidant and antibodies exert their actions via increasing amyloid-beta (Aβ) clearance or decreasing Tau aggregation. Among small molecules, most of them that are present in the last phases act as specific antagonists (Suvorexant, Idalopirdine, Intepirdine, Trazodone, Carvedilol, and Risperidone) or agonists (Dextromethorphan, Resveratrol, Brexpiprazole) and frequently ameliorate cognitive dysfunctions. Conclusion: The presences of a small number of candidates in the last phase suggest that a large number of candidates have had an undesirable side effect or were unable to pass essential eligibility for future phases. Among successful treatment approaches, clearance of Aβ, recovery of cognitive deficits, and control of acute neuroinflammation are widely chosen. It is predicted that some FDA-approved drugs, such as Paracetamol, Risperidone, Escitalopram, Simvastatin, Cilostazoand, and Ritalin-SR, could also be used in off-label ways for AD. This review improves our ability to recognize novel treatments for AD and suggests approaches for the clinical trial design for this devastating disease in the near future.

scholarly journals On the design of early-phase Alzheimer’s disease clinical trials with cerebrospinal fluid tau outcomes

2021 ◽  
pp. 174077452110344
Author(s):  
Michelle M Nuño ◽  
Joshua D Grill ◽  
Daniel L Gillen ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cerebrospinal Fluid ◽  
Phosphorylated Tau ◽  
Inclusion Criteria ◽  
Eligibility Criteria ◽  
Total Tau ◽  
Prodromal Alzheimer’S Disease ◽  
Study Power

Background/Aims: The focus of Alzheimer’s disease studies has shifted to earlier disease stages, including mild cognitive impairment. Biomarker inclusion criteria are often incorporated into mild cognitive impairment clinical trials to identify individuals with “prodromal Alzheimer’s disease” to ensure appropriate drug targets and enrich for participants likely to develop Alzheimer’s disease dementia. The use of these eligibility criteria may affect study power. Methods: We investigated outcome variability and study power in the setting of proof-of-concept prodromal Alzheimer’s disease trials that incorporate cerebrospinal fluid levels of total tau (t-tau) and phosphorylated (p-tau) as primary outcomes and how differing biomarker inclusion criteria affect power. We used data from the Alzheimer’s Disease Neuroimaging Initiative to model trial scenarios and to estimate the variance and within-subject correlation of total and phosphorylated tau. These estimates were then used to investigate the differences in study power for trials considering these two surrogate outcomes. Results: Patient characteristics were similar for all eligibility criteria. The lowest outcome variance and highest within-subject correlation were obtained when phosphorylated tau was used as an eligibility criterion, compared to amyloid beta or total tau, regardless of whether total tau or phosphorylated tau were used as primary outcomes. Power increased when eligibility criteria were broadened to allow for enrollment of subjects with either low amyloid beta or high phosphorylated tau. Conclusion: Specific biomarker inclusion criteria may impact statistical power in trials using total tau or phosphorylated tau as the primary outcome. In concert with other important considerations such as treatment target and population of clinical interest, these results may have implications to the integrity and efficiency of prodromal Alzheimer’s disease trial designs.

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