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.

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

The Correlations between Postmortem Brain Pathologies and Cognitive Dysfunction in Aging and Alzheimer's Disease

2018 ◽  
Vol 15 (5) ◽  
pp. 462-473 ◽  
Author(s):  
Wen-Ying Qiu ◽  
Qian Yang ◽  
Wanying Zhang ◽  
Naili Wang ◽  
Di Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Amyloid Β ◽  
Cognitive Status ◽  
Postmortem Brain ◽  
Braak Stage ◽  
Brain Bank ◽  
General Linear Multivariate Model ◽  
Human Brains

Background: The pathological diagnostic criteria for Alzheimer's disease (AD) updated by the National Institute on Aging-Alzheimer's Association (NIA-AA) in 2012 has been widely adopted, but the clinicopathological relevance remained obscure in Chinese population. Objective: This study aims to investigate the correlations between the antemortem clinical cognitive performances and the postmortem neuropathological changes in the aging and AD brains collected in a human brain bank in China. Method: A total of 52 human brains with antemortem cognitive status information [Everyday Cognition (ECog)] were collected through the willed donation program by CAMS/PUMC Human Brain Bank. Pathological changes were evaluated with the “ABC” score following the guidelines of NIA-AA. The clinicopathological relationship was analyzed with correlation analysis and general linear multivariate model. Results: The general ABC score has a significant correlation with global ECog score (r=0.37, p=0.014) and most of ECog domains. The CERAD score of neuritic plaques (C score) has a significant correlation with global ECog score (r=0.40, p=0.007) and the majority of ECog domains, such as memory (r=0.50, p=0.001), language (r=0.45, p=0.002), visuospatial functions (r=0.31, p=0.040), planning (r=0.35, p=0.021) and organization (r=0.39, p=0.010). The Braak stage of neurofibrillary tangles (NFTs) (B score) has a moderate correlation with memory (r=0.32, p=0.035). The Thal phases of amyloid-β (Aβ) deposits (A score) present no significant correlation with any of ECog domains. Conclusion: In this study, we verified the correlation of postmortem C and B scores, but not the A score with cognition performance in a collection of samples from the Chinese human brain bank.

scholarly journals Integrative glycoproteomics reveals protein N-glycosylation aberrations and glycoproteomic network alterations in Alzheimer’s disease

2020 ◽  
Vol 6 (40) ◽  
pp. eabc5802
Author(s):  
Qi Zhang ◽  
Cheng Ma ◽  
Lih-Shen Chin ◽  
Lian Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Large Scale ◽  
Site Occupancy ◽  
Glycosylation Site ◽  
System Level ◽  
Disease Signatures ◽  
And Control

Protein N-glycosylation plays critical roles in controlling brain function, but little is known about human brain N-glycoproteome and its alterations in Alzheimer’s disease (AD). Here, we report the first, large-scale, site-specific N-glycoproteome profiling study of human AD and control brains using mass spectrometry–based quantitative N-glycoproteomics. The study provided a system-level view of human brain N-glycoproteins and in vivo N-glycosylation sites and identified disease signatures of altered N-glycopeptides, N-glycoproteins, and N-glycosylation site occupancy in AD. Glycoproteomics-driven network analysis showed 13 modules of co-regulated N-glycopeptides/glycoproteins, 6 of which are associated with AD phenotypes. Our analyses revealed multiple dysregulated N-glycosylation–affected processes and pathways in AD brain, including extracellular matrix dysfunction, neuroinflammation, synaptic dysfunction, cell adhesion alteration, lysosomal dysfunction, endocytic trafficking dysregulation, endoplasmic reticulum dysfunction, and cell signaling dysregulation. Our findings highlight the involvement of N-glycosylation aberrations in AD pathogenesis and provide new molecular and system-level insights for understanding and treating AD.

scholarly journals A case study of Alzheimer’s disease with work-related exposure to aluminium

2020 ◽  
Vol 11 (3) ◽  
pp. 4311-4313
Author(s):  
Vityala Yethindra ◽  
Gulzat Mataeva ◽  
Baian Beknazarova ◽  
Furquan Nazami ◽  
Yogesh parihar ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Human Brain ◽  
Large Scale ◽  
Al Content ◽  
Work Related ◽  
The Mean ◽  
Asian Male

Aluminium (Al) is a neurotoxin, and its work-related exposure can lead to Alzheimer’s disease (AD). In this case study, we describe the increased levels of Al in brain samples of a patient diagnosed with AD, who reportedly had work-related exposure to Al. In 2019, an Asian male died at the age of 64 with AD. His brain samples showed increased Al levels. The mean Al presence in tissues (n = 21) was 4.72, with a SD of 3.4 μg/g DW and a range from 0.00 – 18.35 μg/g DW. During his work in a large-scale aluminium factory, his work involved exposure to Al for five years on weekly four days basis. In our case study, we investigated the Al content of brain tissue in AD, and Al levels were seen raised and related to neurodegeneration. Work-related exposure to Al in a patient initially showed mild symptoms then lead to memory issues, and after years he was diagnosed with AD. Many clinical trials and researches are needed to find more possible effects of Al content in the human brain.

Diagnostic Framework for Automatic Classification and Visualization of Alzheimer’s Disease with Feature Extraction Using Wavelet Transform

2021 ◽  
Vol 19 (7) ◽  
pp. 84-95
Author(s):  
M. Anitha ◽  
V. Karpagam ◽  
P. Tamije Selvy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Human Brain ◽  
Parallel Implementation ◽  
Normal Brain

Alzheimer’s Disease (AD) is a serious disease that destroys brain and is classified as the most widespread type of dementia. Manual evaluation of image scans relies on visual reading and semi-quantitative investigation of various human brain sections, leading to wrong diagnoses. Neuroimaging plays a significant part in AD detection, using image processing approaches that succeed the drawback of traditional diagnosis methods. Feature extraction is done through Wavelet Transform (WT). Feature selection is an important step in machine learning, where best features set from all possible features is determined. Mutual Information based feature selection (MI) and Correlation-based Feature Selection (CFS) captures the ‘correlation’ between random variables. Machine Learning techniques are broadly used in a classification problem, as it is simple, effective mechanisms and capability to train to contribute intelligence to the arrangement. Classifiers used in this proposed work are Artificial Neural Network (ANN), Random Forest, Convolutional Neural Network (CNN), and Wavelet-based CNN. The superior ability of ANN is high-speed processing achieved through extensive parallel implementation, and this has emphasized necessity of research in this field. CNN has encouraged tackling this issue. This work proves that wavelet-based CNN performs better with a classification accuracy of 91.87%, the sensitivity of 0.94 for normal brain and 0.88 for AD affected brain, the positive predictive value of 0.91 for normal brain and 0.92 for AD affected brain, and F measure of 0.92 for normal brain and 0.90 for AD affected brain on ADNI MRI dataset of the human brain in detecting AD.

scholarly journals Structures of filaments from Pick’s disease reveal a novel tau protein fold

2018 ◽  
Author(s):  
Benjamin Falcon ◽  
Wenjuan Zhang ◽  
Alexey G. Murzin ◽  
Garib Murshudov ◽  
Holly J. Garringer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Tau Protein ◽  
Neurodegenerative Disorder ◽  
Pick's Disease ◽  
Pick’S Disease ◽  
Pick Bodies ◽  
Molecular Conformations ◽  
Selective Incorporation

The ordered assembly of tau protein into abnormal filamentous inclusions underlies many human neurodegenerative diseases1. Tau assemblies appear to spread through specific neural networks in each disease2, with short filaments having the greatest seeding activity3. The abundance of tau inclusions strongly correlates with disease symptoms4. Six tau isoforms are expressed in normal adult human brain - three isoforms with four microtubule-binding repeats each (4R tau) and three isoforms lacking the second repeat (3R tau)1. In various diseases, tau filaments can be composed of either 3R tau or 4R tau, or of both 3R and 4R tau. They have distinct cellular and neuroanatomical distributions5, with morphological and biochemical differences suggesting that they may be able to adopt disease-specific molecular conformations6,7. Such conformers may give rise to different neuropathological phenotypes8,9, reminiscent of prion strains10. However, the underlying structures are not known. Using electron cryo-microscopy (cryo-EM), we recently reported the structures of tau filaments from Alzheimer’s disease, which contain both 3R and 4R tau11. Here we have determined the structures of tau filaments from Pick’s disease, a neurodegenerative disorder characterised by frontotemporal dementia. They consist of residues K254-F378 of 3R tau, which are folded differently when compared to tau in Alzheimer’s disease filaments, establishing the existence of conformers of assembled tau. The Pick fold explains the selective incorporation of 3R tau in Pick bodies and the differences in phosphorylation relative to the tau filaments of Alzheimer’s disease. Our findings show how tau can adopt distinct folds in human brain in different diseases, an essential step for understanding the formation and propagation of molecular conformers.

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

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.

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