scholarly journals White Matter Network Alterations in Alzheimer’s Disease Patients

2020 ◽  
Vol 10 (3) ◽  
pp. 919
Author(s):  
Ramesh Kumar Lama ◽  
Sang-Woong Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Grey Matter ◽  
Small World ◽  
Brain Network ◽  
Topological Properties ◽  
Graph Theoretical Analysis ◽  
Small World Property ◽  
The Brain

Previous studies have revealed the occurrence of alterations of white matter (WM) and grey matter (GM) microstructures in Alzheimer’s disease (AD) and their prodromal state amnestic mild cognitive impairment (MCI). In general, these alterations can be studied comprehensively by modeling the brain as a complex network, which describes many important topological properties, such as the small-world property, modularity, and efficiency. In this study, we systematically investigated white matter abnormalities using unbiased whole brain network analysis. We compared regional and network related WM features between groups of 19 AD and 25 MCI patients and 22 healthy controls (HC) using tract-based spatial statistics (TBSS), network based statistics (NBS) and graph theoretical analysis. We did not find significant differences in fractional anisotropy (FA) between two groups on TBSS analysis. However, observable alterations were noticed at a network level. Brain network measures such as global efficiency and small world properties were low in AD patients compared to HCs.

scholarly journals Compensatory reconfiguration of functional networks between white matter and grey matter in Alzheimer’s disease

2021 ◽  
Author(s):  
Xingxing Zhang ◽  
Qing Guan ◽  
Debo Dong ◽  
Fuyong Chen ◽  
Jing Yi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Euclidean Distance ◽  
Grey Matter ◽  
Temporal Cortex ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Compensatory Mechanisms ◽  
The Brain

AbstractThe temporal synchronization of BOLD signals within white matter (WM) and between WM and grey matter (GM) exhibited intrinsic architecture and cognitive relevance. However, few studies examined the network property within- and between-tissue in Alzheimer’s disease (AD). The hub regions with high weighted degree (WD) were prone to the neuropathological damage of AD. To systematically investigate the changes of hubs within- and between-tissue functional networks in AD patients, we used the resting-state fMRI data of 30 AD patients and 37 normal older adults (NC) from the ADNI open database, and obtained four types of voxel-based WD metrics and four types of distant-dependent WD metrics (ddWD) based on a series of Euclidean distance ranges with a 20mm increment. We found that AD patients showed decreased within-tissue ddWD in the thalamic nucleus and increased between-tissue ddWD in the occipito-temporal cortex, posterior thalamic radiation, and sagittal stratum, compared to NC. We also found that AD patients showed the increased between-tissue FCs between the posterior thalamic radiation and occipito-temporal cortex, and between the sagittal stratum and the salience and executive networks. The dichotomy of decreased and increased ddWD metrics and their locations were consistent with previous studies on the neurodegnerative and compensatory mechanisms of AD, indicating that despite the disruptions, the brain still strived to compensate for the neural inefficiency by reorganizing functional circuits. Our findings also suggested the short-to-medium ranged ddWD metrics between WM and GM as useful biomarker to detect the compensatory changes of functional networks in AD.

Comparison of the Unified Segmentation Method and the New Segmentation Method on Detection of Grey Matter and White Matter Changes in Alzheimer’s Disease Based on Voxel-Based Morphometry

2011 ◽  
Vol 301-303 ◽  
pp. 1189-1195
Author(s):  
Ling Jing Hu ◽  
Long Zheng Tong ◽  
Yun Yun Duan ◽  
Bo Wu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Brain Atrophy ◽  
Grey Matter ◽  
Segmentation Method ◽  
Voxel Based Morphometry ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Voxel-based morphometry method (VBM) has been widely applied to detect the brain atrophy and achieved promising results; however, the effect of the segmentation step in VBM is not clear and the new segmentation method in SPM8 hasn’t been used in Alzheimer’s disease (AD) studies. The aim of this study is to investigate the locations and degrees of grey matter (GM), white matter (WM) atrophy and evaluate the results derived from two segmentation methods. Magnetic resonance imaging (MRI) was collected in 16 AD patients and 16 healthy controls (HC). Using two segmentation methods respectively, several reduction clusters of GM and WM were detected but the locations and degrees of reduction volumes were discrepant resulted from different segmentation methods. Our results suggest that VBM is an effective tool to analyze AD brain atrophy and based on VBM, the comparison of the locations and degrees of volume reduction among AD researches through different segmentation methods should be cautious.

Abstract 44: Associations of Atrial Fibrillation, Neuroimaging Measures of Cerebrovascular Disease and Alzheimer’S Disease-related Pathology, and Cognitive Impairment

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Jonathan Graff-Radford ◽  
Rosebud Roberts ◽  
Malini Madhavan ◽  
Alejandro Rabinstein ◽  
Ruth Cha ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
White Matter ◽  
Cerebrovascular Disease ◽  
Regression Models ◽  
Grey Matter ◽  
White Matter Hyperintensity ◽  
Carrier Status

The objective of this study was to investigate the cross-sectional associations of atrial fibrillation with neuroimaging measures of cerebrovascular disease and Alzheimer’s disease-related pathology, and their interaction with cognitive impairment. MRI scans of non-demented individuals (n=1044) from the population-based Mayo Clinic Study of Aging were analyzed for infarctions, total grey matter, hippocampal and white matter hyperintensity volumes. A subset of 496 individuals underwent FDG and C-11 Pittsburgh compound B (PiB) PET scans. We assessed the associations of atrial fibrillation with i) categorical MRI measures (cortical and subcortical infarctions) using multivariable logistic regression models, and with ii) continuous MRI measures ( hippocampal, total grey matter, and white matter hyperintensity volumes) and FDG-PET and PiB-PET measures using multivariable linear regression models, and adjusting for confounders. Among participants who underwent MRI (median age, 77.8, 51.6% male), 13.5% had atrial fibrillation. Presence of atrial fibrillation was associated with subcortical infarctions (odds ratio [OR], 1.83; p=0.002), cortical infarctions (OR, 1.91; p=0.03), total grey matter volume (Beta [β], -.025, p<.0001) after controlling for age, education, gender, APOE e4 carrier status, coronary artery disease, diabetes, history of clinical stroke, and hypertension. However, atrial fibrillation was not associated with white matter hyperintensity volume, hippocampal volume, Alzheimer’s pattern of FDG hypometabolism or PiB uptake. There was a significant interaction of cortical infarction (p for interaction=0.004) and subcortical infarction (p for interaction =0.015) with atrial fibrillation with regards to odds of mild cognitive impairment (MCI). Using subjects with no atrial fibrillation and no infarction as the reference, the OR (95% confidence intervals [CI]) for MCI was 2.98 (1.66, 5.35;p = 0.0002) among participants with atrial fibrillation and any infarction, 0.69 (0.36, 1.33;p= 0.27) for atrial fibrillation and no infarction, and 1.50 (0.96, 2.32;p = 0.07) for no atrial fibrillation and any infarction. These data highlight that atrial fibrillation is associated with MCI in the presence of infarctions.

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.

scholarly journals Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer's disease

Brain ◽  
2010 ◽  
Vol 133 (11) ◽  
pp. 3301-3314 ◽  
Author(s):  
N. Villain ◽  
M. Fouquet ◽  
J.-C. Baron ◽  
F. Mezenge ◽  
B. Landeau ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Grey Matter ◽  
Metabolic Abnormalities ◽  
Early Alzheimer’S Disease

scholarly journals The trend of disruption in the functional brain network topology of Alzheimer’s disease

2021 ◽  
Author(s):  
Alireza Fathian ◽  
Yousef Jamali ◽  
Mohammad Reza Raoufy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Small World ◽  
Brain Network ◽  
Cognitively Normal ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Network ◽  
Progressive Disorder

Abstract Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study analyzed the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression, and these trends behaved differently at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, The methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

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.

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