scholarly journals Alzheimer’s disease under the purview of Graph Theory Centric Genetic Networks

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Yegnanarayanan Venkatraman ◽  
◽  
Narayanaa Y Krithicaa ◽  
Valentina E. Balas ◽  
Marius M. Balas ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Network Theory ◽  
Memory Loss ◽  
Amyloid Plaque ◽  
Brain Regions ◽  
Protein Protein Interaction ◽  
Communication Demands ◽  
The Brain

Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

scholarly journals 3265 Analysis of High-Dimensional Patient Data in Characterizing Alzheimer’s Disease Progression

2019 ◽  
Vol 3 (s1) ◽  
pp. 3-3
Author(s):  
Daniel Baer ◽  
Andrew B. Lawson ◽  
Brandon Vaughan ◽  
Jane E. Joseph
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Cognitive Decline ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Eigenvector Centrality ◽  
Research Hypothesis ◽  
The Brain ◽  
Over Time

OBJECTIVES/SPECIFIC AIMS: Our research hypothesis is that resting state fMRI (rsfMRI) data can be used to identify regions of the brain which are associated with cognitive decline in patients – thereby providing a tool by which to characterize AD progression in patients. METHODS/STUDY POPULATION: We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to analyze Mini-Mental State Examination (MMSE) questionnaire scores from 14 patients diagnosed with AD at two measurement occasions. RsfMRI data was available at the first of these occasions for these patients. These rsfMRI data were summarized into 264 node-based graph theory measures of clustering coefficient and eigenvector centrality. To address our research hypothesis, we modeled changes in patient MMSE scores over time as a function of these rsfMRI data, controlling for relevant confounding factors. This model accounted for the high-dimensionality of our predictor data, the longitudinal nature of the outcome, and our desire to identify a subset of regions in the brain most associated with the MMSE outcome. RESULTS/ANTICIPATED RESULTS: The use of either the clustering coefficient or eigenvector centrality rsfMRI predictors in modeling MMSE scores for patients over time resulted in the identification of different subsets of brain regions associated with cognitive decline. This suggests that these predictors capture different information on patient propensity for cognitive decline. Further work is warranted to validate these results on a larger sample of ADNI patients. DISCUSSION/SIGNIFICANCE OF IMPACT: We conclude that different rsfMRI graph theory measures capture different aspects of cognitive function and decline in patients, which could be a future consideration in clinical practice.

scholarly journals Brain geometry matters in Alzheimer’s disease progression: a simulation study

2020 ◽  
Author(s):  
Masoud Hoore ◽  
Jeffrey Kelling ◽  
Mahsa Sayadmanesh ◽  
Tanmay Mitra ◽  
Marta Schips ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Activity ◽  
Amyloid Β ◽  
Amyloid Plaque ◽  
Brain Regions ◽  
Plaque Formation ◽  
Secretion Rate ◽  
Heterogeneous Distribution ◽  
The Brain

AbstractThe Amyloid cascade hypothesis (ACH) for Alzheimer’s disease (AD) is modeled over the whole brain tissue with a set of partial differential equations. Our results show that the amyloid plaque formation is critically dependent on the secretion rate of amyloid β (Aβ), which is proportional to the product of neural density and neural activity. Neural atrophy is similarly related to the secretion rate of Aβ. Due to a heterogeneous distribution of neural density and brain activity throughout the brain, amyloid plaque formation and neural death occurs heterogeneously in the brain. The geometry of the brain and microglia migration in the parenchyma bring more complexity into the system and result in a diverse amyloidosis and dementia pattern of different brain regions. Although the pattern of amyloidosis in the brain cortex from in-silico results is similar to experimental autopsy findings, they mismatch at the central regions of the brain, suggesting that ACH is not able to explain the whole course of AD without considering other factors, such as tau-protein aggregation or neuroinflammation.

Current Strategies and Novel Drug Approaches for Alzheimer Disease

2020 ◽  
Vol 19 (9) ◽  
pp. 676-690 ◽  
Author(s):  
Roma Ghai ◽  
Kandasamy Nagarajan ◽  
Meenakshi Arora ◽  
Parul Grover ◽  
Nazakat Ali ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cholinergic Neurons ◽  
Amyloid Plaque ◽  
Neural Pathways ◽  
Current Trends ◽  
Personality Changes ◽  
Traditional Therapies ◽  
Novel Drug ◽  
The Brain

Alzheimer’s Disease (AD) is a chronic, devastating dysfunction of neurons in the brain leading to dementia. It mainly arises due to neuronal injury in the cerebral cortex and hippocampus area of the brain and is clinically manifested as a progressive mental failure, disordered cognitive functions, personality changes, reduced verbal fluency and impairment of speech. The pathology behind AD is the formation of intraneuronal fibrillary tangles, deposition of amyloid plaque and decline in choline acetyltransferase and loss of cholinergic neurons. Tragically, the disease cannot be cured, but its progression can be halted. Various cholinesterase inhibitors available in the market like Tacrine, Donepezil, Galantamine, Rivastigmine, etc. are being used to manage the symptoms of Alzheimer’s disease. The paper’s objective is to throw light not only on the cellular/genetic basis of the disease, but also on the current trends and various strategies of treatment including the use of phytopharmaceuticals and nutraceuticals. Enormous literature survey was conducted and published articles of PubMed, Scifinder, Google Scholar, Clinical Trials.org and Alzheimer Association reports were studied intensively to consolidate the information on the strategies available to combat Alzheimer’s disease. Currently, several strategies are being investigated for the treatment of Alzheimer’s disease. Immunotherapies targeting amyloid-beta plaques, tau protein and neural pathways are undergoing clinical trials. Moreover, antisense oligonucleotide methodologies are being approached as therapies for its management. Phytopharmaceuticals and nutraceuticals are also gaining attention in overcoming the symptoms related to AD. The present review article concludes that novel and traditional therapies simultaneously promise future hope for AD treatment.

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

scholarly journals ADeditome provides the genomic landscape of A-to-I RNA editing in Alzheimer’s disease

2021 ◽  
Author(s):  
Sijia Wu ◽  
Mengyuan Yang ◽  
Pora Kim ◽  
Xiaobo Zhou
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rna Editing ◽  
Memory Loss ◽  
Brain Regions ◽  
Disease Stage ◽  
Mirna Regulation ◽  
Annotation Database ◽  
Functional Annotations ◽  
Genomic Landscape

Abstract A-to-I RNA editing, contributing to nearly 90% of all editing events in human, has been reported to involve in the pathogenesis of Alzheimer’s disease (AD) due to its roles in brain development and immune regulation, such as the deficient editing of GluA2 Q/R related to cell death and memory loss. Currently, there are urgent needs for the systematic annotations of A-to-I RNA editing events in AD. Here, we built ADeditome, the annotation database of A-to-I RNA editing in AD available at https://ccsm.uth.edu/ADeditome, aiming to provide a resource and reference for functional annotation of A-to-I RNA editing in AD to identify therapeutically targetable genes in an individual. We detected 1676 363 editing sites in 1524 samples across nine brain regions from ROSMAP, MayoRNAseq and MSBB. For these editing events, we performed multiple functional annotations including identification of specific and disease stage associated editing events and the influence of editing events on gene expression, protein recoding, alternative splicing and miRNA regulation for all the genes, especially for AD-related genes in order to explore the pathology of AD. Combing all the analysis results, we found 108 010 and 26 168 editing events which may promote or inhibit AD progression, respectively. We also found 5582 brain region-specific editing events with potentially dual roles in AD across different brain regions. ADeditome will be a unique resource for AD and drug research communities to identify therapeutically targetable editing events. Significance: ADeditome is the first comprehensive resource of the functional genomics of individual A-to-I RNA editing events in AD, which will be useful for many researchers in the fields of AD pathology, precision medicine, and therapeutic researches.

scholarly journals Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Angela M. Crist ◽  
Kelly M. Hinkle ◽  
Xue Wang ◽  
Christina M. Moloney ◽  
Billie J. Matchett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Digital Pathology ◽  
Selective Vulnerability ◽  
Brain Regions ◽  
Biochemical Analyses ◽  
The Brain ◽  
Relevant Gene ◽  
Tau Expression

AbstractSelective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

Intranasal administration of mitochondria improves spatial memory in olfactory bulbectomized mice

2021 ◽  
pp. 153537022110568
Author(s):  
Natalia V Bobkova ◽  
Daria Y Zhdanova ◽  
Natalia V Belosludtseva ◽  
Nikita V Penkov ◽  
Galina D Mironova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Memory ◽  
Intranasal Administration ◽  
Brain Structures ◽  
Brain Regions ◽  
Dependent Manner ◽  
Behavioral Experiments ◽  
Hippocampal Cell Culture ◽  
The Brain

Here, we found that functionally active mitochondria isolated from the brain of NMRI donor mice and administrated intranasally to recipient mice penetrated the brain structures in a dose-dependent manner. The injected mitochondria labeled with the MitoTracker Red localized in different brain regions, including the neocortex and hippocampus, which are responsible for memory and affected by degeneration in patients with Alzheimer's disease. In behavioral experiments, intranasal microinjections of brain mitochondria of native NMRI mice improved spatial memory in the olfactory bulbectomized (OBX) mice with Alzheimer’s type degeneration. Control OBX mice demonstrated loss of spatial memory tested in the Morris water maze. Immunocytochemical analysis revealed that allogeneic mitochondria colocalized with the markers of astrocytes and neurons in hippocampal cell culture. The results suggest that a non-invasive route intranasal administration of mitochondria may be a promising approach to the treatment of neurodegenerative diseases characterized, like Alzheimer's disease, by mitochondrial dysfunction.

scholarly journals Ceramide and Related-Sphingolipid Levels Are Not Altered in Disease-Associated Brain Regions of APPSLand APPSL/PS1M146LMouse Models of Alzheimer's Disease: Relationship with the Lack of Neurodegeneration?

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Laurence Barrier ◽  
Bernard Fauconneau ◽  
Anastasia Noël ◽  
Sabrina Ingrand
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Models ◽  
Neuronal Death ◽  
Time Course ◽  
Neuronal Loss ◽  
Brain Regions ◽  
App Processing ◽  
Ceramide Accumulation ◽  
The Brain

There is evidence linking sphingolipid abnormalities, APP processing, and neuronal death in Alzheimer's disease (AD). We previously reported a strong elevation of ceramide levels in the brain of the APPSL/PS1Ki mouse model of AD, preceding the neuronal death. To extend these findings, we analyzed ceramide and related-sphingolipid contents in brain from two other mouse models (i.e., APPSLand APPSL/PS1M146L) in which the time-course of pathology is closer to that seen in most currently available models. Conversely to our previous work, ceramides did not accumulate in disease-associated brain regions (cortex and hippocampus) from both models. However, the APPSL/PS1Ki model is unique for its drastic neuronal loss coinciding with strong accumulation of neurotoxic Aβisoforms, not observed in other animal models of AD. Since there are neither neuronal loss nor toxic Aβspecies accumulation in APPSLmice, we hypothesized that it might explain the lack of ceramide accumulation, at least in this model.

Sign in / Sign up

Export Citation Format

Share Document