Altered brain network measures in patients with primary writing tremor

2017 ◽  
Vol 59 (10) ◽  
pp. 1021-1029 ◽  
Author(s):  
Abhishek Lenka ◽  
Ketan Ramakant Jhunjhunwala ◽  
Rajanikant Panda ◽  
Jitender Saini ◽  
Rose Dawn Bharath ◽  
...  
Keyword(s):  
Brain Network ◽  
Network Measures ◽  
Primary Writing Tremor

scholarly journals BrainNetVis: An Open-Access Tool to Effectively Quantify and Visualize Brain Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Eleni G. Christodoulou ◽  
Vangelis Sakkalis ◽  
Vassilis Tsiaras ◽  
Ioannis G. Tollis
Keyword(s):  
Open Access ◽  
Brain Network ◽  
Global Network ◽  
Control Group ◽  
Network Modelling ◽  
Network Information ◽  
Network Measures ◽  
Graph Metrics ◽  
Network Metrics ◽  
The Brain

This paper presents BrainNetVis, a tool which serves brain network modelling and visualization, by providing both quantitative and qualitative network measures of brain interconnectivity. It emphasizes the needs that led to the creation of this tool by presenting similar works in the field and by describing how our tool contributes to the existing scenery. It also describes the methods used for the calculation of the graph metrics (global network metrics and vertex metrics), which carry the brain network information. To make the methods clear and understandable, we use an exemplar dataset throughout the paper, on which the calculations and the visualizations are performed. This dataset consists of an alcoholic and a control group of subjects.

scholarly journals Borderline personality disorder classification based on brain network measures during emotion regulation

2020 ◽  
Author(s):  
Henk Cremers ◽  
Linda van Zutphen ◽  
Sascha Duken ◽  
Gregor Domes ◽  
Andreas Sprenger ◽  
...  
Keyword(s):  
Emotion Regulation ◽  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Brain Network ◽  
Borderline Personality ◽  
Future Research ◽  
Support Vector ◽  
Classification Analysis ◽  
Network Analyses ◽  
Network Measures

AbstractBorderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.

Is Transient Global Amnesia a Network Disease?

2018 ◽  
Vol 80 (5-6) ◽  
pp. 345-354 ◽  
Author(s):  
Kang Min Park ◽  
Byung In Lee ◽  
Sung Eun Kim
Keyword(s):  
White Matter ◽  
Healthy Subjects ◽  
Transient Global Amnesia ◽  
Brain Network ◽  
Network Measures ◽  
Postcentral Gyrus ◽  
Frontal Operculum ◽  
Global Amnesia ◽  
The Right ◽  
The Brain

Background: We evaluated a brain network using graph theoretical analysis and microstructural abnormalities of the white matter in patients with transient global amnesia (TGA). Methods: Twenty patients with TGA and healthy control subjects were recruited, and they underwent diffusion tensor imaging (DTI) scans. Graph theory was applied to obtain network measures based on DTI data. We investigated the network measures and microstructural abnormalities of white matter using tract-based spatial statistics (TBSS) analysis in the patients with TGA. Results: Measures of global topology were not different between the patients with TGA and healthy subjects. However, there were significant differences of hubs organization; the strength of the right superior and inferior orbitofrontal, the right inferior frontal operculum, the left superior parietal, and left postcentral gyrus, the cluster coefficient of the right middle orbitofrontal and left inferior parietal gyrus, the betweenness centrality of the left angular gyrus, and the pagerank centrality of the right superior and inferior orbitofrontal, right inferior frontal operculum, left superior parietal, and left postcentral gyrus in the patients with TGA were significantly lower than those in healthy subjects. Regarding the analysis of the white matter microstructure with TBSS, there were no differences in the fractional anisotropy and mean diffusivity values between the 2 groups. Conclusions: We newly identify a reorganization of network hubs of the brain network in patients with TGA, especially in the regions of the default-mode network. These alterations of the brain network may play a role in the pathophysiologic mechanism underlying TGA and suggest that TGA is a network disease.

P4-277: CHANGES IN BRAIN NETWORK MEASURES IN PRE-SYMPTOMATIC ALZHEIMER'S DISEASE WITH E280A PRESENILIN-1 MUTATION GENE

2014 ◽  
Vol 10 ◽  
pp. P885-P886
Author(s):  
Carlos Tobon ◽  
Jon Edinson Duque ◽  
John Fredy Ochoa ◽  
Mauricio Hernandez ◽  
Yakeel T. Quiroz ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Network ◽  
Presenilin 1 ◽  
Network Measures ◽  
Presenilin 1 Mutation

scholarly journals Quantifying uncertainty in brain network measures using Bayesian connectomics

2014 ◽  
Vol 8 ◽  
Author(s):  
Ronald J. Janssen ◽  
Max Hinne ◽  
Tom Heskes ◽  
Marcel A. J. van Gerven
Keyword(s):  
Brain Network ◽  
Network Measures ◽  
Quantifying Uncertainty

scholarly journals Novel genetic variants associated with brain functional networks in 18,445 adults from the UK Biobank

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Heidi Foo ◽  
Anbupalam Thalamuthu ◽  
Jiyang Jiang ◽  
Forrest C. Koch ◽  
Karen A. Mather ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Brain Network ◽  
Functional Network ◽  
Chromosome 2 ◽  
Uk Biobank ◽  
Network Graph ◽  
Network Measures ◽  
Functional Brain Network ◽  
Significant Locus ◽  
The Uk

AbstractHere, we investigated the genetics of weighted functional brain network graph theory measures from 18,445 participants of the UK Biobank (44–80 years). The eighteen measures studied showed low heritability (mean h2SNP = 0.12) and were highly genetically correlated. One genome-wide significant locus was associated with strength of somatomotor and limbic networks. These intergenic variants were located near the PAX8 gene on chromosome 2. Gene-based analyses identified five significantly associated genes for five of the network measures, which have been implicated in sleep duration, neuronal differentiation/development, cancer, and susceptibility to neurodegenerative diseases. Further analysis found that somatomotor network strength was phenotypically associated with sleep duration and insomnia. Single nucleotide polymorphism (SNP) and gene level associations with functional network measures were identified, which may help uncover novel biological pathways relevant to human brain functional network integrity and related disorders that affect it.

scholarly journals Structural brain network characteristics in patients with episodic and chronic migraine

2021 ◽  
Author(s):  
Lars Michels ◽  
Nabin Koirala ◽  
Sergiu Groppa ◽  
Roger Luechinger ◽  
Andreas R Gantenbein ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Integrity ◽  
Primary Headache ◽  
Headache Disorder ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Global Network ◽  
Support Vector ◽  
Clinical Parameters ◽  
Network Measures

Abstract Background: Migraine is a primary headache disorder that can be classified into an episodic (EM) and a chronic form (CM). Network analysis within the graph-theoretical framework based on connectivity patterns provides an approach to observe large-scale structural integrity. We test the hypothesis that migraineurs are characterized by a segregated network. Methods: 19 healthy controls (HC), 17 EM patients and 12 CM patients were included. Cortical thickness and subcortical volumes were computed, and topology was analyzed using a graph theory analytical framework and network-based statistics. We further used support vector machines regression (SVR) to identify whether these network measures were able to predict clinical parameters. Results: Network based statistics revealed significantly lower interregional connectivity strength between anatomical compartments including the fronto-temporal, parietal and visual areas in EM and CM when compared to HC. Higher assortativity was seen in both patients’ group, with higher modularity for CM and higher transitivity for EM compared to HC. For subcortical networks, higher assortativity and transitivity were observed for both patients’ group with higher modularity for CM. SVR revealed that network measures could robustly predict clinical parameters for migraineurs. Conclusion: We found global network disruption for EM and CM indicated by highly segregated network in migraine patients compared to HC. Higher modularity but lower clustering coefficient in CM is suggestive of more segregation in this group compared to EM. The presence of a segregated network could be a sign of maladaptive reorganization of headache related brain circuits, leading to migraine attacks or secondary alterations to pain.

scholarly journals Borderline Personality Disorder classification based on brain network centrality during emotion regulation

2019 ◽  
Author(s):  
Henk Cremers ◽  
Linda van Zutphen ◽  
Sascha Béla Duken ◽  
Gregor Domes ◽  
Andreas Sprenger ◽  
...  
Keyword(s):  
Emotion Regulation ◽  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Null Model ◽  
Brain Network ◽  
Borderline Personality ◽  
Future Research ◽  
Support Vector ◽  
Network Analyses ◽  
Network Measures

Background: Borderline Personality Disorder is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the key neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Methods: Patients with borderline personality disorder (n=51), cluster C personality disorder (n=26) and non-patient controls (n=44) performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task related connectivity: phasic refers to task-event dependent changes in connectivity while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency and participation coefficient) and then entered as separate models in a nested cross-validated linear support vector machine classification analysis. Results: Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p=0.23. Exploratory analysis did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Conclusions: Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analyses. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.

Sign in / Sign up

Export Citation Format

Share Document