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.

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

2019 ◽  
Author(s):  
Si Shuaizong ◽  
Wang Bin ◽  
Liu Xiao ◽  
Yu Chong ◽  
Ding Chao ◽  
...  
Keyword(s):  
Mutual Information ◽  
Brain Networks ◽  
Brain Network ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Connection Probability ◽  
Network Topologies ◽  
Cortical Regions ◽  
Anatomical Space ◽  
The Brain

Abnormal connections in brain networks of healthy people always bring the problems of cognitive impairments and degeneration of specific brain circuits, which may finally result in Alzheimer’s disease (AD). Exploring the development of the brain from normal controls (NC) to AD is an essential part of human research. Although connections changes have been found in the development, the connection mechanism that drives these changes remain incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncover the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a model named MINM from the perspective of topology-based mutual information 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. Experiment results 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.

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 Brain Network Alterations in Alzheimer’s Disease Identified by Early-Phase PIB-PET

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Liping Fu ◽  
Linwen Liu ◽  
Jinming Zhang ◽  
Baixuan Xu ◽  
Yong Fan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Phase ◽  
Brain Networks ◽  
Normal Subjects ◽  
Brain Network ◽  
Highly Correlated ◽  
Fdg Pet Ct ◽  
Cognitively Normal Subjects ◽  
The Brain

The aim of this study was to identify the brain networks from early-phase 11C-PIB (perfusion PIB, pPIB) data and to compare the brain networks of patients with differentiating Alzheimer’s disease (AD) with cognitively normal subjects (CN) and of mild cognitively impaired patients (MCI) with CN. Forty participants (14 CN, 12 MCI, and 14 AD) underwent 11C-PIB and 18F-FDG PET/CT scans. Parallel independent component analysis (pICA) was used to identify correlated brain networks from the 11C-pPIB and 18F-FDG data, and a two-sample t-test was used to evaluate group differences in the corrected brain networks between AD and CN, and between MCI and CN. Our study identified a brain network of perfusion (early-phase 11C-PIB) that highly correlated with a glucose metabolism (18F-FDG) brain network and colocalized with the default mode network (DMN) in an AD-specific neurodegenerative cohort. Particularly, decreased 18F-FDG uptake correlated with a decreased regional cerebral blood flow in the frontal, parietal, and temporal regions of the DMN. The group comparisons revealed similar spatial patterns of the brain networks derived from the 11C-pPIB and 18F-FDG data. Our findings indicate that 11C-pPIB derived from the early-phase 11C-PIB could provide complementary information for 18F-FDG examination in AD.

scholarly journals EARLIER DETECTION OF ALZHEIMER’S DISEASE USING IMAGE PROCESSING AND MACHINE LEARNING ALGORITHMS WITH GRAPH THEORY

2021 ◽  
Vol 10 (8) ◽  
pp. 36-40
Author(s):  
D.J. Samatha Naidu ◽  
G. Anand Kumar Reddy
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Thinking Skills ◽  
Brain Network ◽  
Machine Learning Algorithms ◽  
Brain Disorder ◽  
Automated Method ◽  
The Brain

Alzheimer’s disease is one of the brain disease which is irreversible, progressive brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. There is no cure for Alzheimer’s disease but we prevent it’s by early detection. In existing work, limited with Alzheimer’s are irreversible, effect on daily activities, high memory loss and reducing the size of brain, etc. previous works focused on 2D and 3D formats now we considering 4D images. In proposed work, this work aims to present an automated method that assists in the diagnosis of Alzheimer’s disease supports the monitoring of the progression of the disease. The study of brain network based on resting-state functional Magnetic Resonance Imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize various aspects of the brain network by calculating measures the accuracy of different machine learning methods and different features to classify Cognitively Normal (C.N) individuals from Alzheimer’s Disease (A.D) and to predict longitudinal outcomes in participants with Mild Cognitive Impairment (MCI).

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

scholarly journals Group-level progressive alterations in brain connectivity patterns revealed by diffusion-tensor brain networks across severity stages in Alzheimer’s disease

2017 ◽  
Author(s):  
J. Rasero ◽  
C. Alonso-Montes ◽  
I. Diez ◽  
L. Olabarrieta-Landa ◽  
L. Remaki ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Severity ◽  
Diffusion Tensor ◽  
Brain Networks ◽  
Stage Ii ◽  
Group Level ◽  
The Brain

AbstractAlzheimer’s disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1=36, healthy control subjects, Control), G2 (N2=36, early mild cognitive impairment, EMCI), G3 (N3=36, late mild cognitive impairment, LMCI) and G4 (N4=36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I 3(Control vs EMCI), stage II (Control vs LMCI) and stage III (Control vs AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were three-fold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks,including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.

scholarly journals Brain connectivity during Alzheimer’s disease progression and its cognitive impact in a transgenic rat model

2019 ◽  
Author(s):  
Emma Muñoz-Moreno ◽  
Raúl Tudela ◽  
Xavier López-Gil ◽  
Guadalupe Soria
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Transgenic Animals ◽  
Brain Network ◽  
Cognitive Outcome ◽  
Structural Network ◽  
New Treatments

ABSTRACTThe research of Alzheimer’s disease (AD) in their early stages and its progression till symptomatic onset is essential to understand the pathology and investigate new treatments. Animal models provide a helpful approach to this research, since they allow for controlled follow-up during the disease evolution. In this work, transgenic TgF344-AD rats were longitudinally evaluated starting at 6 months of age. Every 3 months, cognitive abilities were assessed by a memory-related task and magnetic resonance imaging (MRI) was acquired. Structural and functional brain networks were estimated and characterized by graph metrics to identify differences between the groups in connectivity, its evolution with age, and its influence on cognition. Structural networks of transgenic animals were altered since the earliest stage. Likewise, aging significantly affected network metrics in TgF344-AD, but not in the control group. In addition, while the structural brain network influenced cognitive outcome in transgenic animals, functional network impacted how control subjects performed. TgF344-AD brain network alterations were present from very early stages, difficult to identify in clinical research. Likewise, the characterization of aging in these animals, involving structural network reorganization and its effects on cognition, opens a window to evaluate new treatments for the disease.AUTHOR SUMMARYWe have applied magnetic resonance image based connectomics to characterize TgF344-AD rats, a transgenic model of Alzheimer’s disease (AD). This represents a highly translational approach, what is essential to investigate potential treatments. TgF344-AD animals were evaluated from early to advanced ages to describe alterations in brain connectivity and how brain networks are affected by age. Results showed that aging had a bigger impact in the structural connectivity of the TgF344-AD than in control animals, and that changes in the structural network, already observed at early ages, significantly influenced cognitive outcome of transgenic animals. Alterations in connectivity were similar to the described in AD human studies, and complement them providing insights into earlier stages and a plot of AD effects throughout the whole life span.

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

2017 ◽  
Vol 9 (4) ◽  
pp. 46-66 ◽  
Author(s):  
Bhuvaneshwari Bhaskaran ◽  
Kavitha Anandan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Disorder ◽  
Default Mode ◽  
Graph Metrics ◽  
Functional Topology ◽  
The Brain

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

scholarly journals Evidence of compensation in the brain networks of Lewy body dementia and Alzheimer’s disease patients

2017 ◽  
Author(s):  
Luis R. Peraza ◽  
Ruth Cromarty ◽  
Xenia Kobeleva ◽  
Michael J. Firbank ◽  
Alison Killen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lewy Body ◽  
Minimum Spanning Tree ◽  
Brain Networks ◽  
Lewy Bodies ◽  
Lewy Body Dementia ◽  
Dominant Frequency ◽  
Compensatory Response ◽  
The Brain

AbstractDementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) require differential management despite presenting with symptomatic overlap. A human electrophysiological difference is a decrease of dominant frequency (DF) −the highest power frequency between 4-15Hz– in DLB; a characteristic of Parkinsonian diseases. We analysed electroencephalographic (EEG) recordings from old adults: healthy controls (HCs), AD, DLB and Parkinson’s disease dementia (PDD) patients. Brain networks were assessed with the minimum spanning tree (MST) within six EEG bands: delta, theta, high-theta, alpha, beta and DF. Patients showed lower alpha band connectivity and lower DF than HCs. Lewy body dementias showed a randomised MST compared with HCs and AD in high-theta and alpha but not within the DF. The MST randomisation in DLB and PDD reflects decreased brain efficiency as well as impaired neural synchronisation. However, the lack of network topology differences at the DF indicates a compensatory response of the brain to the neuropathology.

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.

Sign in / Sign up

Export Citation Format

Share Document