scholarly journals MR g-ratio-weighted connectome analysis in patients with multiple sclerosis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Koji Kamagata ◽  
Andrew Zalesky ◽  
Kazumasa Yokoyama ◽  
Christina Andica ◽  
Akifumi Hagiwara ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Network Topology ◽  
Diffusion Mri ◽  
Myelinated Axon ◽  
Brain Network ◽  
Disconnection Syndrome ◽  
Enhanced Sensitivity ◽  
G Ratio ◽  
The Brain ◽  
Limbic Regions

Abstract Multiple sclerosis (MS) is a brain network disconnection syndrome. Although the brain network topology in MS has been evaluated using diffusion MRI tractography, the mechanism underlying disconnection in the disorder remains unclear. In this study, we evaluated the brain network topology in MS using connectomes with connectivity strengths based on the ratio of the inner to outer myelinated axon diameter (i.e., g-ratio), thereby providing enhanced sensitivity to demyelination compared with the conventional measures of connectivity. We mapped g-ratio-based connectomes in 14 patients with MS and compared them with those of 14 age- and sex-matched healthy controls. For comparison, probabilistic tractography was also used to map connectomes based on the number of streamlines (NOS). We found that g-ratio- and NOS-based connectomes comprised significant connectivity reductions in patients with MS, predominantly in the motor, somatosensory, visual, and limbic regions. However, only the g-ratio-based connectome enabled detection of significant increases in nodal strength in patients with MS. Finally, we found that the g-ratio-weighted nodal strength in motor, visual, and limbic regions significantly correlated with inter-individual variation in measures of disease severity. The g-ratio-based connectome can serve as a sensitive biomarker for diagnosing and monitoring disease progression.

scholarly journals Drug-resistant focal epilepsy in children is associated with increased modal controllability of the whole brain and epileptogenic regions

2021 ◽  
Author(s):  
Kiran Seunarine ◽  
Xiaosong He ◽  
Martin Tisdall ◽  
Christopher Clark ◽  
Danielle S Bassett ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Focal Epilepsy ◽  
Brain Network ◽  
Structure And Function ◽  
Network Control ◽  
Drug Resistant ◽  
Drug Resistant Epilepsy ◽  
And Function ◽  
The Brain ◽  
Disease Understanding

Network control theory provides a framework by which neurophysiological dynamics of the brain can be modelled as a function of the structural connectome constructed from diffusion MRI. Average controllability describes the ability of a region to drive the brain to easy-to-reach neurophysiological states whilst modal controllability describes the ability of a region to drive the brain to difficult-to-reach states. In this study, we identify increases in mean average and modal controllability in children with drug-resistant epilepsy compared to healthy controls. Using simulations, we purport that these changes may be a result of increased thalamocortical connectivity. At the node level, we demonstrate decreased modal controllability in the thalamus and posterior cingulate regions. In those undergoing resective surgery, we also demonstrate increased modal controllability of the resected parcels, a finding specific to patients who were rendered seizure free following surgery. Changes in controllability are a manifestation of brain network dysfunction in epilepsy and may be a useful construct to understand the pathophysiology of this archetypical network disease. Understanding the mechanisms underlying these controllability changes may also facilitate the design of network-focussed interventions that seek to normalise network structure and function.

scholarly journals Communicability distance reveals hidden patterns of Alzheimer disease

2020 ◽  
Author(s):  
Eufemia Lella ◽  
Ernesto Estrada
Keyword(s):  
Alzheimer Disease ◽  
Path Length ◽  
Brain Regions ◽  
Brain Network ◽  
Efficient System ◽  
Brain Hemispheres ◽  
Disconnection Syndrome ◽  
Path Lengths ◽  
The Brain ◽  
Better Than

AbstractThe communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer disease. Using this distance we obtain the shortest communicability path lengths between different regions of brain networks from Alzheimer diseased (AD) patients and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only and 20% affects vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of Alzheimer disease. Finally, we show that in 76.9% damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

scholarly journals Measuring Robustness of Brain Networks in Autism Spectrum Disorder with Ricci Curvature

2019 ◽  
Author(s):  
Anish K. Simhal ◽  
Kimberly L.H. Carpenter ◽  
Saad Nadeem ◽  
Joanne Kurtzberg ◽  
Allen Song ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Ricci Curvature ◽  
Diffusion Mri ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Global Changes ◽  
Before And After ◽  
The Brain

ABSTRACTRicci curvature is a method for measuring the robustness of networks. In this work, we use Ricci curvature to measure robustness of brain networks affected by autism spectrum disorder (ASD). Subjects with ASD are given a stem cell infusion and are imaged with diffusion MRI before and after the infusion. By using Ricci curvature to measure changes in robustness, we quantify both local and global changes in the brain networks correlated with the infusion. Our results find changes in regions associated with ASD that were not detected via traditional brain network analysis.

Cognitive Reserve Modifies the Brain Network Underlying Cognitive Fatigue in Multiple Sclerosis (MS) (P03.076)

Neurology ◽  
2012 ◽  
Vol 78 (Meeting Abstracts 1) ◽  
pp. P03.076-P03.076
Author(s):  
A. Das ◽  
G. Wylie ◽  
H. Genova ◽  
J. Sumowski ◽  
J. DeLuca ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Reserve ◽  
Brain Network ◽  
Cognitive Fatigue ◽  
The Brain

scholarly journals Communicability distance reveals hidden patterns of Alzheimer’s disease

2020 ◽  
Vol 4 (4) ◽  
pp. 1007-1029 ◽  
Author(s):  
Eufemia Lella ◽  
Ernesto Estrada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Path Length ◽  
Brain Regions ◽  
Brain Network ◽  
Efficient System ◽  
Brain Hemispheres ◽  
Disconnection Syndrome ◽  
Path Lengths ◽  
The Brain

The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer’s disease. Using this distance, we obtain the shortest communicability path lengths between different regions of brain networks from patients with Alzheimer’s disease (AD) and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach, we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only, and 20% affect vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of AD. Finally, we show that in 76.9% of damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

Structural connectivity in multiple sclerosis and modeling of disconnection

2019 ◽  
Vol 26 (2) ◽  
pp. 220-232 ◽  
Author(s):  
Elisabetta Pagani ◽  
Maria A Rocca ◽  
Ermelinda De Meo ◽  
Mark A Horsfield ◽  
Bruno Colombo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Structural Connectivity ◽  
Progressive Multiple Sclerosis ◽  
Brain Network ◽  
Magnetic Resonance Imaging Mri ◽  
Cortical Regions ◽  
Diffusion Tensors ◽  
Relapsing Remitting Multiple Sclerosis ◽  
Network Metrics ◽  
The Brain

Background: Multiple sclerosis (MS) is characterized by focal white matter damage, and when the brain is modeled as a network, lesions can be treated as disconnection events. Objective: To evaluate whether modeling disconnection caused by lesions helps explain motor and cognitive impairment in MS. Methods: Pathways connecting 116 cortical regions were reconstructed with magnetic resonance imaging (MRI) tractography from diffusion tensors averaged across healthy controls (HCs); maps of pathways were applied to 227 relapse-onset MS patients and 50 HCs to derive structural connectivity. Then, the likelihood of individual connections passing through lesions was used to model disconnection. Patients were grouped according to clinical phenotype (113 relapsing-remitting multiple sclerosis (RRMS), 69 secondary progressive multiple sclerosis (SPMS), 45 benign MS), and then network metrics were compared between groups (analysis of variance (ANOVA)) and correlated with motor and cognitive scores (linear regression). Results: Global metrics differentiated RRMS from SPMS and benign MS patients, but not benign from SPMS patients. Nodal connectivity strength replicated global results. After disconnection, few nodes were significantly different between benign MS and RRMS patients. Correlations revealed nodes pertinent to motor and cognitive dysfunctions; these became slightly stronger after disconnection. Conclusion: Connectivity did not change greatly after modeled disconnection, suggesting that the brain network is robust against damage caused by MS lesions.

scholarly journals Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity

2021 ◽  
pp. 101000
Author(s):  
Moriah E. Thomason ◽  
Ava C. Palopoli ◽  
Nicki N. Jariwala ◽  
Denise M. Werchan ◽  
Alan Chen ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Brain Network Connectivity ◽  
The Brain

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

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