scholarly journals Deep Learning for Alzheimer's Disease: Mapping Large-scale Histological Tau Protein for Neuroimaging Biomarker Validation

NeuroImage ◽  
2021 ◽  
pp. 118790
Author(s):  
Daniela Ushizima ◽  
Yuheng Chen ◽  
Maryana Alegro ◽  
Dulce Ovando ◽  
Rana Eser ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Tau Protein ◽  
Large Scale ◽  
Disease Mapping ◽  
Biomarker Validation

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

P3-236 A novel method for large-scale preparation of truncated tau protein involved in pathogenesis of Alzheimer's disease

2004 ◽  
Vol 25 ◽  
pp. S422
Author(s):  
Natalia Csokova ◽  
Rostislav Skrabana ◽  
Hans-Dieter Liebig ◽  
Anna Mederlyova ◽  
Peter Kontsek ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Tau Protein ◽  
Large Scale ◽  
Large Scale Preparation ◽  
Scale Preparation ◽  
Novel Method

scholarly journals Deep Learning for Alzheimer’s Disease: Mapping Large-scale Histological Tau Protein for Neuroimaging Biomarker Validation

2019 ◽  
Author(s):  
Maryana Alegro ◽  
Yuheng Chen ◽  
Dulce Ovando ◽  
Helmut Heinser ◽  
Rana Eser ◽  
...  
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Tau Protein ◽  
Large Scale ◽  
Digital Pathology ◽  
Symptomatic Disease ◽  
Density Maps ◽  
Human Brains

AbstractDeposits of abnormal tau protein inclusions in the brain are a pathological hallmark of Alzheimer’s disease (AD), and are the best predictor of neuronal loss and clinical decline, but have been limited to postmortem assessment. Imaging-based biomarkers to detect tau deposits in vivo could leverage AD diagnosis and monitoring beginning in pre-symptomatic disease stages. Several PET tau tracers are available for research studies, but validation of such tracers against direct detection of tau deposits in brain tissue remains incomplete because of methodological limitations. Confirmation of the biological basis of PET binding requires large-scale voxel-to-voxel correlation has been challenging because of the dimensionality of the whole human brain histology data, deformation caused by tissue processing that precludes registration, and the need to process terabytes of information to cover the whole human brain volume at microscopic resolution. In this study, we created a computational pipeline for segmenting tau inclusions in billion-pixel digital pathology images of whole human brains, aiming at generating quantitative, tridimensional tau density maps that can be used to decipher the distribution of tau inclusions along AD progression and validate PET tau tracers. Our pipeline comprises several pre- and post-processing steps developed to handle the high complexity of these brain digital pathology images. SlideNet, a convolutional neural network designed to process our large datasets to locate and segment tau inclusions, is at the core of the pipeline. Using our novel method, we have successfully processed over 500 slides from two whole human brains, immunostained for two phospho-tau antibodies (AT100 and AT8) spanning several Gigabytes of images. Our artificial neural network estimated strong tau inclusion from image segmentation, which performs with ROC AUC of 0.89 and 0.85 for AT100 and AT8, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. Furthermore, our pipeline successfully created 3D tau inclusion density maps that were co-registered to the histology 3D maps.

scholarly journals Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer's Disease Classification

2018 ◽  
Author(s):  
Xinyang Feng ◽  
Jie Yang ◽  
Zachary C Lipton ◽  
Scott A Small ◽  
Frank A Provenzano
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Hippocampal Formation ◽  
Characteristic Curve ◽  
Disease Classification ◽  
Brain Volumes ◽  
Regional Analyses

Deep learning techniques on MRI scans have demonstrated great potential to improve the diagnosis of neurological diseases. Here, we investigate the application of 3D deep convolutional neural networks (CNNs) for classifying Alzheimer's disease (AD) based on structural MRI data. In particular, we take on two challenges that are under-explored in the literature on deep learning for neuroimaging. First deep neural networks typically require large-scale data that is not always available in medical studies. Therefore, we explore the use of including longitudinal scans in classification studies, greatly increasing the amount of data for training and improving the generalization performance of our classifiers. Moreover, previous studies applying deep learning to classifying Alzheimer's disease from neuroimaging have typically addressed classification based on whole brain volumes but stopped short of performing in-depth regional analyses to localize the most predictive areas. Additionally, we show a deep net trained to distinguish between AD and cognitively normal subjects can be applied to classify mild cognitive impairment patients, with classification scores aligning empirically with the likelihood of progression to AD. Our initial results demonstrate both that we can classify AD with an area under the receiver operator characteristic curve (AUROC) of .990 and that we can predict conversion to AD among patients in the MCI subgroup with an AURUC of 0.787. We then localize the predictive regions, by performing both saliency-based interpretation and rigorous slice and lobar level ablation studies. Interestingly, our regional analyses identified the hippocampal formation, including the entorhinal cortex, to be the most predictive region for our models. This finding adds evidence that the hippocampal formation is an anatomical seat of AD and a prominent feature in its diagnosis. Together, the results of this study further demonstrate the potential of deep learning to impact AD classification and to identify AD's structural neuroimaging signatures. The proposed classification and regional analyses methods constitute a general framework that can easily be applied to other disorders and imaging modalities.

Large-scale, multicenter study of cerebrospinal fluid tau protein phosphorylated at serine 199 for the antemortem diagnosis of Alzheimer's disease

2001 ◽  
Vol 50 (2) ◽  
pp. 150-156 ◽  
Author(s):  
Nobuo Itoh ◽  
Hiroyuki Arai ◽  
Katsuya Urakami ◽  
Koichi Ishiguro ◽  
Hideto Ohno ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Multicenter Study ◽  
Tau Protein ◽  
Large Scale

scholarly journals The Association between Tau Protein Level in Cerebrospinal Fluid and Cognitive Status: A Large-Scale Analysis of GAAIN Database

2021 ◽  
Vol 11 (7) ◽  
pp. 861
Author(s):  
Kyle Eckhoff ◽  
Robert Morris ◽  
Valeria Zuluaga ◽  
Rebecca Polsky ◽  
Feng Cheng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Tau Protein ◽  
Protein Level ◽  
Large Scale ◽  
Cognitive Status ◽  
Scale Analysis ◽  
Total Tau ◽  
Large Scale Analysis

Alzheimer’s disease (AD) and the associated neurodegenerative dementia have become of increasing concern in healthcare. The tau protein has been considered a key hallmark of progressive neurodegeneration. In this paper, a large-scale analysis of five datasets (more than 2500 people) from the Global Alzheimer’s Association Interactive Network (GAAIN) databases was performed to investigate the association between the level of tau protein, including total tau and phosphorylated tau (p-tau), in cerebrospinal fluid (CSF) and cognitive status. Statistically significant (or marginally significant) high total tau or p-tau concentrations in CSF were observed in dementia patients compared with healthy people in all datasets. There is also a statistically significant (or marginally significant) negative correlation between p-tau concentrations in CSF and Folstein Mini-Mental State Examination (MMSE) scores. In addition, transcriptomic data derived from mouse microglial cells showed multiple genes upregulated in Toll-like receptor signaling and Alzheimer’s disease pathways, including TNF, TLR2, IL-1β, and COX subunits, suggesting that the mechanism of action that relates p-tau and MMSE scores may be through overactivation of pro-inflammatory microglial activity by Aβ peptides, TNF-mediated hyperphosphorylation of tau, and the infectious spread of pathological tau across healthy neurons. Our results not only confirmed the association between tau protein level and cognitive status in a large population but also provided useful information for the understanding of the role of tau in neurodegeneration and the development of dementia.

Dehydroepiandrosterone (DHEA) and its Sulphate (DHEAS) in Alzheimer’s Disease

2020 ◽  
Vol 17 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Dubravka S. Strac ◽  
Marcela Konjevod ◽  
Matea N. Perkovic ◽  
Lucija Tudor ◽  
Gordana N. Erjavec ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Therapeutic Potential ◽  
Relevant Literature ◽  
Comprehensive Overview ◽  
Brain Functions ◽  
Specific Effects ◽  
And Behavior

Background: Neurosteroids Dehydroepiandrosterone (DHEA) and Dehydroepiandrosterone Sulphate (DHEAS) are involved in many important brain functions, including neuronal plasticity and survival, cognition and behavior, demonstrating preventive and therapeutic potential in different neuropsychiatric and neurodegenerative disorders, including Alzheimer’s disease. Objective: The aim of the article was to provide a comprehensive overview of the literature on the involvement of DHEA and DHEAS in Alzheimer’s disease. Method: PubMed and MEDLINE databases were searched for relevant literature. The articles were selected considering their titles and abstracts. In the selected full texts, lists of references were searched manually for additional articles. Results: We performed a systematic review of the studies investigating the role of DHEA and DHEAS in various in vitro and animal models, as well as in patients with Alzheimer’s disease, and provided a comprehensive discussion on their potential preventive and therapeutic applications. Conclusion: Despite mixed results, the findings of various preclinical studies are generally supportive of the involvement of DHEA and DHEAS in the pathophysiology of Alzheimer’s disease, showing some promise for potential benefits of these neurosteroids in the prevention and treatment. However, so far small clinical trials brought little evidence to support their therapy in AD. Therefore, large-scale human studies are needed to elucidate the specific effects of DHEA and DHEAS and their mechanisms of action, prior to their applications in clinical practice.

Targeting Tau Hyperphosphorylation via Kinase Inhibition: Strategy to Address Alzheimer's Disease

2020 ◽  
Vol 20 (12) ◽  
pp. 1059-1073 ◽  
Author(s):  
Ahmad Abu Turab Naqvi ◽  
Gulam Mustafa Hasan ◽  
Md. Imtaiyaz Hassan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Tau Protein ◽  
Glycogen Synthase ◽  
Paired Helical Filaments ◽  
Tau Hyperphosphorylation ◽  
Microtubule Stabilization ◽  
Extracellular Signal

Microtubule-associated protein tau is involved in the tubulin binding leading to microtubule stabilization in neuronal cells which is essential for stabilization of neuron cytoskeleton. The regulation of tau activity is accommodated by several kinases which phosphorylate tau protein on specific sites. In pathological conditions, abnormal activity of tau kinases such as glycogen synthase kinase-3 β (GSK3β), cyclin-dependent kinase 5 (CDK5), c-Jun N-terminal kinases (JNKs), extracellular signal-regulated kinase 1 and 2 (ERK1/2) and microtubule affinity regulating kinase (MARK) lead to tau hyperphosphorylation. Hyperphosphorylation of tau protein leads to aggregation of tau into paired helical filaments like structures which are major constituents of neurofibrillary tangles, a hallmark of Alzheimer’s disease. In this review, we discuss various tau protein kinases and their association with tau hyperphosphorylation. We also discuss various strategies and the advancements made in the area of Alzheimer's disease drug development by designing effective and specific inhibitors for such kinases using traditional in vitro/in vivo methods and state of the art in silico techniques.

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