Biological Motion Coding in the Brain: Analysis of Visually Driven EEG Functional Networks
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Herein, we address the time evolution of brain functional networks computed from electroencephalographic activity driven by visual stimuli. We describe how these functional network signatures change in fast scale when confronted with point-light display stimuli depicting biological motion (BM) as opposed to scrambled motion (SM). Whereas global network measures (average path length, average clustering coefficient, and average betweenness) computed as a function of time did not discriminate between BM and SM, local node properties did. Comparing the network local measures of the BM condition with those of the SM condition, we found higher degree and betweenness values in the left frontal (F7) electrode, as well as a higher clustering coefficient in the right occipital (O2) electrode, for the SM condition. Conversely, for the BM condition, we found higher degree values in central parietal (Pz) electrode and a higher clustering coefficient in the left parietal (P3) electrode. These results are discussed in the context of the brain networks involved in encoding BM versus SM.Keywords:
Clustering coefficient
Stroke is a kind of common and frequently occurred disease.So far,there has been no certain conclusion of mechanism after stroke.This study aims to study the differences of topological properties in the whole brain functional network between stroke patients and the healthy controls using event related fMRI.Seven stroke patients and 6 healthy people were involved in the present study,3T MRI scanner was employed.The time series of each AAL brain region were extracted using DPARSF software.Each brain region was made as a node,and Pearson correlation was used to calculate the correlation coefficients between any two brain regions.Then an adjacency matrix was obtained and the whole brain network was established.The important parameters of network,such as global efficiency,clustering coefficient,and node degree and betweenness centrality were calculated and averaged among each group.Experimental results showed that the global efficiency(Patients: 0.496 7;Healthy: 0.586 9),clustering coefficient(Patients: 0.418 9;Healthy: 0.476 8),and node degree of the network for patients,were lower than those for healthy group.The betweenness centrality of patients appeared 0 in some nodes,however there was no such nodes which illustrate 0 in healthy group.It may suggest that the brain network of patients tends to a generalized network,and the efficiency of that was reduced evidently.And also it can be inferred that there are breakpoints in patients' brain network.
Clustering coefficient
Stroke
Adjacency matrix
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Based on a statistical analysis on the papers published by university librarians in 19 core periodicals in the field of LIS,this paper introduced the research methodologies of social network analysis and analyzed several basic topological properties of university libraries cooperation network,such as degree distribution,average path degree,clustering coefficient,and betweenness centrality.The paper also used G-N clustering algorithm to made a cluster analysis on cooperative network and summarized the basic modes of inter-agency co-operation behavior of university libraries.
Clustering coefficient
Social Network Analysis
Average path length
Social network (sociolinguistics)
Network Analysis
Degree distribution
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We study cascading failures in smart grids, where an attacker selectively compromises the nodes with probabilities proportional to their degrees, betweenness, or clustering coefficient. This implies that nodes with high degrees, betweenness, or clustering coefficients are attacked with higher probability. We mathematically and experimentally analyze the sizes of the giant components of the networks under different types of targeted attacks, and compare the results with the corresponding sizes under random attacks. We show that networks disintegrate faster for targeted attacks compared to random attacks. A targeted attack on a small fraction of high degree nodes disintegrates one or both of the networks, whereas both the networks contain giant components for random attack on the same fraction of nodes. An important observation is that an attacker has an advantage if it compromises nodes based on their betweenness, rather than based on degree or clustering coefficient. We next study adaptive attacks, where an attacker compromises nodes in rounds. Here, some nodes are compromised in each round based on their degree, betweenness or clustering coefficients, instead of compromising all nodes together. In this case, the degree, betweenness, or clustering coefficient is calculated before the start of each round, instead of at the beginning. We show experimentally that an adversary has an advantage in this adaptive approach, compared to compromising the same number of nodes all at once.
Clustering coefficient
Degree (music)
Giant component
Fraction (chemistry)
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A single session of heart rate variability (HRV) biofeedback in apparently healthy young people and adolescents aged 14-17 years in order to increase vagal effects on heart rhythm and also electroencephalograms were carried out. Different variants of EEG spectral power during the successful HRV biofeedback session were identified. In the case of I variant of EEG activity the increase of power spectrum of alpha-, betal-, theta-components takes place in all parts of the brain. In the case of II variant of EEG activity the reduction of power spectrum of alpha-, betal-, theta-activity in all parts of the brain was observed. I and II variants of EEG activity cause more intensive regime of cortical-subcortical interactions. During the III variant of EEG activity the successful biofeedback is accompanied by increase of alpha activity in the central, front and anteriofrontal brain parts and so indicates the formation of thalamocortical relations of neural network in order to optimize the vegetal regulation of heart function. There was an increase in alpha- and beta1-activity in the parietal, central, frontal and temporal brain parts during the IV variant of EEG activity and so that it provides the relief of neural networks communication for information processing. As a result of V variance of EEG activity there was the increase of power spectrum of theta activity in the central and frontal parts of both cerebral hemispheres, so it was associated with the cortical-hippocampal interactions to achieve a successful biofeedback.
Biofeedback
Alpha (finance)
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To study the sentiment diffusion of online public opinions about hot events, we collected people's posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative (N). These sentiments are combined into modes through coarse graining. We constructed sentiment mode complex network of online public opinions (SMCOP) with modes as nodes and the conversion relation in chronological order between different types of modes as edges. We calculated the strength, k-plex clique, clustering coefficient and betweenness centrality of the SMCOP. The results show that the strength distribution obeys power law. Most posts' sentiments are weakly positive and neutral, whereas few are strongly negative. There are weakly positive subgroups and neutral subgroups with ppppp and ooooo as the core mode, respectively. Few modes have larger betweenness centrality values and most modes convert to each other with these higher betweenness centrality modes as mediums. Therefore, the relevant person or institutes can take measures to lead people's sentiments regarding online hot events according to the sentiment diffusion mechanism.
Clustering coefficient
Clique
Mode (computer interface)
Sentiment Analysis
Value (mathematics)
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Clustering coefficient
Closeness
Prestige
Degree distribution
Giant component
Social network (sociolinguistics)
Social Network Analysis
Motif (music)
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EEG is a brain imaging called electroencephalography (EEG) that measures and displays brain activities with signals. Brain-Computer Interface (BCI), on the other hand, are devices that allow their users to interact with computers only through brain activity. This activity is usually measured by EEG. Brain Computer Interface applications base their functionality either on observing the user's state or allowing the user to submit their ideas. EEG signals are gathered from the different brain regions with the international 10-20 electrode system as multichannel and the structure of them is very complex. Therefore, the EEG signals need to be analyzed for understanding and recognizing. In this study, the time-frequency characteristics of the EEG signal for the realization of a specific thought or action related to palm opening and closing were investigated. The EEG frequency band and the EEG electrodes (brain region) where the activity can be strongly traced were identified.
Identification
SIGNAL (programming language)
Interface (matter)
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The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.
Cascading failure
Clustering coefficient
Robustness
Average path length
Scale-free network
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The funa is a prevalent concept in Chile that aims to expose a person’s bad behavior, punish the aggressor publicly, and warn the community about it. Despite its massive use on the social networks of Chilean society, the real dissemination of funas among communities is unknown. In this paper, we extract, generate, analyze, and compare the Twitter social network’s spread of three tweets related to “funas” against three other trending topics, through the analysis of global network characteristics over time (degree distribution, clustering coefficient, hop plot, and betweenness centrality). As observed, funas have a specific behavior, and they disseminate as quickly as a common tweet or more quickly; however, they spread thanks to several network users, generating a cohesive group.
Clustering coefficient
Plot (graphics)
Social Network Analysis
Degree distribution
Social network (sociolinguistics)
Network Analysis
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EEG-fMRI
Human brain
Neurophysiology
Brain Function
Eyes open
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