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.

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

2020 ◽  
Vol 4 (2) ◽  
pp. 397-415
Author(s):  
Emma Muñoz-Moreno ◽  
Raúl Tudela ◽  
Xavier López-Gil ◽  
Guadalupe Soria
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transgenic Animals ◽  
Brain Network ◽  
Control Group ◽  
Transgenic Rat ◽  
Disease Evolution ◽  
Early Stages ◽  
Graph Metrics ◽  
New Treatments

The research of Alzheimer’s disease (AD) in its 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.

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 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.

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.

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

2021 ◽  
pp. 589-610
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 Theoretic ◽  
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 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 A paradigm for longitudinal complex network analysis over patient cohorts in neuroscience

2019 ◽  
Vol 7 (2) ◽  
pp. 196-214
Author(s):  
Heather Shappell ◽  
Yorghos Tripodis ◽  
Ronald J. Killiany ◽  
Eric D. Kolaczyk
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Brain Networks ◽  
Network Dynamics ◽  
Brain Network ◽  
Cross Sectional ◽  
Functional Connections ◽  
Network Change ◽  
Study Designs

AbstractThe study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Many of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs and an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models to the neuroscience setting. Stochastic Actor-Oriented Models are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e. network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state functional magnetic resonance imaging setting with data from the Alzheimer’s Disease Neuroimaging Initiative study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with Alzheimer’s disease.

scholarly journals Subtypes of functional brain connectivity as early markers of neurodegeneration in Alzheimer’s disease

2017 ◽  
Author(s):  
Pierre Orban ◽  
Angela Tam ◽  
Sebastian Urchs ◽  
Melissa Savard ◽  
Cécile Madjar ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Brain Connectivity ◽  
Brain Network ◽  
List Type ◽  
Healthy Older Adults ◽  
Functional Brain ◽  
Default Mode ◽  
Functional Brain Network

HighlightsReliable functional brain network subtypes accompany cognitive impairment in ADSymptom-related subtypes exist in the default-mode, limbic and salience networksA limbic subtype is associated with a familial risk of AD in healthy older adultsLimbic subtypes also associate with beta amyloid deposition and ApoE4In BriefWe found reliable subtypes of functional brain connectivity networks in older adults, associated with AD-related clinical symptoms in patients as well as several AD risk factors/biomarkers in asymptomatic individuals.SummaryThe heterogeneity of brain degeneration has not been investigated yet for functional brain network connectivity, a promising biomarker of Alzheimer’s disease. We coupled cluster analysis with resting-state functional magnetic resonance imaging to discover connectivity subtypes in healthy older adults and patients with cognitive disorders related to Alzheimer’s disease, noting associations between subtypes and cognitive symptoms in the default-mode, limbic and salience networks. In an independent asymptomatic cohort with a family history of Alzheimer’s dementia, the connectivity subtypes had good test-retest reliability across all tested networks. We found that a limbic subtype was overrepresented in these individuals, which was previously associated with symptoms. Other limbic subtypes showed associations with cerebrospinal fluid Aβ1-42levels and ApoE4 genotype. Our results demonstrate the existence of reliable subtypes of functional brain networks in older adults and support future investigations in limbic connectivity subtypes as early biomarkers of Alzheimer’s degeneration.

Sign in / Sign up

Export Citation Format

Share Document