scholarly journals A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0128136 ◽  
Author(s):  
Chenhui Hu ◽  
Lin Cheng ◽  
Jorge Sepulcre ◽  
Keith A. Johnson ◽  
Georges E. Fakhri ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regression Model ◽  
Brain Connectivity ◽  
Spectral Graph

Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Yunlong Nie ◽  
Eugene Opoku ◽  
Laila Yasmin ◽  
Yin Song ◽  
Jie Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Resting State ◽  
Brain Connectivity ◽  
Parametric Bootstrap ◽  
Imaging Genetics ◽  
Nucleotide Polymorphisms ◽  
Mixed Effect ◽  
Default Mode

AbstractWe conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

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 Multimodal imaging brain connectivity analysis (MIBCA) toolbox: preliminary application to Alzheimer’s disease

2014 ◽  
Vol 1 (S1) ◽  
Author(s):  
Andre Santos Ribeiro ◽  
Luís Miguel Lacerda ◽  
Nuno André da Silva ◽  
Hugo Alexandre Ferreira
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multimodal Imaging ◽  
Brain Connectivity ◽  
Connectivity Analysis ◽  
Preliminary Application

Identifying Genetic related Neuroimaging Biomarkers of Alzheimer's Disease via Diagnosis-Guided Group Sparse Multitask Learning Method

2021 ◽  
Vol 16 ◽  
Author(s):  
Anshi Lin ◽  
Wei Kong ◽  
Shuaiqun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regression Model ◽  
Multivariate Regression ◽  
Prior Information ◽  
Diagnostic Information ◽  
Multitask Learning ◽  
Learning Method ◽  
Pathological Characteristics ◽  
Different Types

Background: Advances in brain imaging and high-throughput genotyping techniques have provided new methods for studying the effects of genetic variation on brain structure and function. Traditionally, a variety of prior information has been added into the multivariate regression method for single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) to improve the accuracy of prediction. In previous studies, brain regions of interest (ROIs) with different types of pathological characteristics (Alzheimer's Disease/Mild Cognitive Impairment/healthy control) can only be randomly dispersed in test cases, greatly limiting the prediction ability of the regression model and failing to obtain optimal global results. Objective: This study proposes a multivariate regression model informed by prior diagnostic information to overcome this limitation. Method: In the prediction model, we first consider traditional prior information and then design a new regularization form to integrate the diagnostic information of different sample ROIs into the model. Results: Experiments demonstrated that this method greatly improves the prediction accuracy of the model compared to other methods and selects a batch of promising pathogenic SNP loci. Conclusion: Taking into account that ROIs with different types of pathological characteristics can be employed as prior information, we propose a new method (Diagnosis-Guided Group Sparse Multitask Learning Method) that improves the ability to predict disease-related quantitative feature sites and select genetic feature factors, applying this model to research on the pathogenesis of Alzheimer's disease.

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.

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