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.

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 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.

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 Neuroticism alters the transcriptome of the frontal cortex to contribute to the cognitive decline and onset of Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Céline H. De Jager ◽  
Charles C. White ◽  
David A. Bennett ◽  
Yiyi Ma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Brain Regions ◽  
Personality Characteristics ◽  
Tau Pathology ◽  
Postmortem Human Brain ◽  
Brain Transcriptome ◽  
Neo Five Factor Inventory ◽  
The Impact

AbstractAccumulating evidence has suggested that the molecular transcriptional mechanism contributes to Alzheimer’s disease (AD) and its endophenotypes of cognitive decline and neuropathological traits, β-amyloid (Aβ) and phosphorylated tangles (TAU). However, it is unknown what is the impact of the AD risk factors, personality characteristics assessed by the NEO Five-Factor Inventory, on the human brain’s transcriptome. Using postmortem human brain samples from 466 subjects, we found that neuroticism has a significant overall impact on the brain transcriptome (omnibus P = 0.005) but not the other four personality characteristics. Focused on those cognitive decline related gene co-expressed modules, neuroticism has nominally significant associations (P < 0.05) with four neuronal modules, which are more related to PHFtau than Aβ across all eight brain regions. Furthermore, the effect of neuroticism on cognitive decline and AD might be mediated through the expression of module 7 and TAU pathology (P = 0.008). To conclude, neuroticism has a broad impact on the transcriptome of human brains, and its effect on cognitive decline and AD may be mediated through gene transcription programs related to TAU pathology.

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.

scholarly journals FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

2021 ◽  
Author(s):  
Hessam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Permutation Test ◽  
Pearson Correlation ◽  
Functional Integration ◽  
Clustering Coefficient ◽  
Kernel Trick ◽  
Non Linear ◽  
The Brain

Abstract Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.

scholarly journals Factors underlying cognitive decline in old age and Alzheimer’s disease: the role of the hippocampus

2017 ◽  
Vol 28 (7) ◽  
pp. 705-714 ◽  
Author(s):  
Wafa Jaroudi ◽  
Julia Garami ◽  
Sandra Garrido ◽  
Michael Hornberger ◽  
Szabolcs Keri ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Personality Traits ◽  
Old Age ◽  
Brain Regions ◽  
Personality Factors ◽  
Associated Symptoms ◽  
Lifestyle Patterns

AbstractThere are many factors that strongly influence the aetiology, development, and progression of cognitive decline in old age, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). These factors include not only different personality traits and moods but also lifestyle patterns (e.g. exercise and diet) and awareness levels that lead to cognitive decline in old age. In this review, we discuss how personality traits, mood states, and lifestyle impact brain and behaviour in older adults. Specifically, our review shows that these lifestyle and personality factors affect several brain regions, including the hippocampus, a region key for memory that is affected by cognitive decline in old age as well as AD. Accordingly, appropriate recommendations are presented in this review to assist individuals in decreasing chances of MCI, dementia, AD, and associated symptoms.

scholarly journals Brain Network Modeling Based on Mutual Information and Graph Theory for Predicting the Connection Mechanism in the Progression of Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 300 ◽  
Author(s):  
Shuaizong Si ◽  
Bin Wang ◽  
Xiao Liu ◽  
Chong Yu ◽  
Chao Ding ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Mutual Information ◽  
Brain Networks ◽  
Brain Network ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Anatomical Space ◽  
The Brain

Alzheimer’s disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiments show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

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