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 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.

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.

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 Virtual connectomic datasets in Alzheimer’s Disease and aging using whole-brain network dynamics modelling

eNeuro ◽  
2021 ◽  
pp. ENEURO.0475-20.2021
Author(s):  
Lucas Arbabyazd ◽  
Kelly Shen ◽  
Zheng Wang ◽  
Martin Hofmann-Apitius ◽  
Petra Ritter ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Dynamics ◽  
Brain Network ◽  
Whole Brain ◽  
Dynamics Modelling

scholarly journals P2-339: ASSOCIATION BETWEEN BRAIN NETWORK DYNAMICS AND PLASMA BIOMARKERS IN COGNITIVELY INTACT INDIVIDUALS AT RISK FOR ALZHEIMER'S DISEASE

2019 ◽  
Vol 15 ◽  
pp. P722-P723
Author(s):  
Patrizia Andrea Chiesa ◽  
Marion Houot ◽  
Andrea Vergallo ◽  
Enrica Cavedo ◽  
Simone Lista ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Network Dynamics ◽  
Brain Network ◽  
Plasma Biomarkers ◽  
Cognitively Intact

scholarly journals A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer’s Disease Data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Suprateek Kundu ◽  
◽  
Joshua Lukemire ◽  
Yikai Wang ◽  
Ying Guo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Network ◽  
Global Network ◽  
Local Network ◽  
Cross Sectional ◽  
Whole Brain ◽  
Time Points ◽  
One Year ◽  
Network Metrics

AbstractThere is well-documented evidence of brain network differences between individuals with Alzheimer’s disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.

scholarly journals Regional cerebral blood flow correlates of visuospatial tasks in Alzheimer's disease

2008 ◽  
Vol 14 (6) ◽  
pp. 1034-1045 ◽  
Author(s):  
WILLIAM J. TIPPETT ◽  
SANDRA E. BLACK
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebral Perfusion ◽  
Right Hemisphere ◽  
Single Photon ◽  
Photon Emission ◽  
Brain Network ◽  
Control Group ◽  
Cross Sectional ◽  
Visuospatial Tasks

AbstractThis study investigated the role of visuospatial tasks in identifying cognitive decline in patients with Alzheimer's disease (AD), by correlating neuropsychological performance with cerebral perfusion measures. There were 157 participants: 29 neurologically healthy controls (age: 70.3 ± 6.6, MMSE ≥ 27), 86 patients with mild AD (age: 69.18 ± 8.28, MMSE ≥ 21) and 42 patients moderate/severe AD (age: 68.86 ± 10.69, MMSE 8–20). Single Photon Emission Computerized Tomography (SPECT) was used to derive regional perfusion ratios, and correlated using partial least squares (PLS) with neuropsychological test scores from the Benton Line Orientation (BLO) and the Rey-Osterrieth Complex Figure (RO). Cross-sectional analysis demonstrated that mean scores differed in accordance with disease status: control group (BLO 25.5, RO 33.3); mild AD (BLO 20.1, RO 25.5); moderate/severe AD (BLO 10.7, RO 16). Correlations were observed between BLO/RO and right parietal SPECT regions in the AD groups. Visuospatial performance, often undersampled in cognitive batteries for AD, is clearly impaired even in mild AD and correlates with functional deficits as indexed by cerebral perfusion ratios on SPECT implicating right hemisphere circuits. Furthermore, PLS reveals that usual spatial tasks probe a distributed brain network in both hemispheres including many areas targeted by early AD pathology. (JINS, 2008, 14, 1034–1045.)

[P3-294]: CROSS-SECTIONAL AND LONGITUDINAL BRAIN NETWORK ATROPHY IN DIFFERENT STAGES OF ALZHEIMER's DISEASE AND IN RELATION TO COGNITIVE MEASURES

2017 ◽  
Vol 13 (7S_Part_22) ◽  
pp. P1055-P1056
Author(s):  
Joyce van Arendonk ◽  
Laura E.M. Wisse ◽  
Sandhitsu R. Das ◽  
Nicholas J. Tustison ◽  
David A. Wolk
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Network ◽  
Cognitive Measures ◽  
Cross Sectional
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