scholarly journals Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG

2021 ◽  
Vol 1 ◽  
Author(s):  
Christos Koutlis ◽  
Vasilios K. Kimiskidis ◽  
Dimitris Kugiumtzis
Keyword(s):  
Small World ◽  
Brain Network ◽  
Discriminative Ability ◽  
Scale Free ◽  
Deterministic Systems ◽  
Epileptiform Discharges ◽  
Eeg Data ◽  
Network Estimation ◽  
Source Level ◽  
Electroencephalogram Eeg

The usage of methods for the estimation of the true underlying connectivity among the observed variables of a system is increasing, especially in the domain of neuroscience. Granger causality and similar concepts are employed for the estimation of the brain network from electroencephalogram (EEG) data. Also source localization techniques, such as the standardized low resolution electromagnetic tomography (sLORETA), are widely used for obtaining more reliable data in the source space. In this work, connectivity structures are estimated in the sensor and in the source space making use of the sLORETA transformation for simulated and for EEG data with episodes of spontaneous epileptiform discharges (ED). From the comparative simulation study on high-dimensional coupled stochastic and deterministic systems originating in the sensor space, we conclude that the structure of the estimated causality networks differs in the sensor space and in the source space. Moreover, different network types, such as random, small-world and scale-free, can be better discriminated on the basis of the data in the original sensor space than on the transformed data in the source space. Similarly, in EEG epochs containing epileptiform discharges, the discriminative ability of network topological indices was significantly better in the sensor compared to the source level. In conclusion, causality networks constructed at the sensor and source level, for both simulated and empirical data, exhibit significant structural differences. These observations indicate that further studies are warranted in order to clarify the exact relationship between data registered in the sensor and source space.

Study of Loss of Alertness and Driver Fatigue Using Visibility Graph Synchronization

2016 ◽  
pp. 171-193 ◽  
Author(s):  
Anwesha Sengupta ◽  
Sibsambhu Kar ◽  
Aurobinda Routray
Keyword(s):  
Sleep Deprivation ◽  
Complex Network ◽  
Human Subjects ◽  
Small World ◽  
Brain Network ◽  
Visibility Graph ◽  
Eeg Data ◽  
Lack Of Sleep ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is widely used to predict performance degradation of human subjects due to mental or physical fatigue. Lack of sleep or insufficient quality or quantity of sleep is one of the major reasons of fatigue. Analysis of fatigue due to sleep deprivation using EEG synchronization is a promising field of research. The present chapter analyses advancing levels of fatigue in human drivers in a sleep-deprivation experiment by studying the synchronization between EEG data. A Visibility Graph Similarity-based method has been employed to quantify the synchronization, which has been formulated in terms of a complex network. The change in the parameters of the network has been analyzed to find the variation of connectivity between brain areas and hence to trace the increase in fatigue levels of the subjects. The parameters of the brain network have been compared with those of a complex network with a random degree of connectivity to establish the small-world nature of the brain network.

scholarly journals Analysis of Epileptic Seizures with Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yan Ni ◽  
Yinghua Wang ◽  
Tao Yu ◽  
Xiaoli Li
Keyword(s):  
Network Models ◽  
Small World ◽  
Epileptic Seizures ◽  
Neural Model ◽  
Brain Network ◽  
Large Area ◽  
Cortical Networks ◽  
Scale Free ◽  
Multiple Channel ◽  
Functional Brain Network

Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple channel EEG recordings based on regular, small world, random, and scale-free network models. Whether the connection patterns of cortical networks are directly associated with the epileptic seizures was investigated. The results showed that small world and scale-free cortical networks are highly correlated with the occurrence of epileptic seizures. In particular, the property of small world network is more significant during the epileptic seizures.

scholarly journals Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

2022 ◽  
Vol 27 (1) ◽  
pp. 1-30
Author(s):  
Mengke Ge ◽  
Xiaobing Ni ◽  
Xu Qi ◽  
Song Chen ◽  
Jinglei Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Small World ◽  
Brain Network ◽  
Graph Processing ◽  
Hop Count ◽  
Scale Free ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

EXPLORING THE VULNERABILITY OF FRACTAL COMPLEX NETWORKS THROUGH CONNECTION PATTERN AND FRACTAL DIMENSION

Fractals ◽  
2019 ◽  
Vol 27 (06) ◽  
pp. 1950102
Author(s):  
DONG-YAN LI ◽  
XING-YUAN WANG ◽  
PENG-HE HUANG
Keyword(s):  
Fractal Dimension ◽  
Quantitative Research ◽  
Fractal Dimensions ◽  
Network Models ◽  
Small World ◽  
Brain Network ◽  
Complex Model ◽  
Scale Free ◽  
Connection Pattern ◽  
The Stability

The structure of network has a significant impact on the stability of the network. It is useful to reveal the effect of fractal structure on the vulnerability of complex network since it is a ubiquitous feature in many real-world networks. There have been many studies on the stability of the small world and scale-free models, but little has been down on the quantitative research on fractal models. In this paper, the vulnerability was studied from two perspectives: the connection pattern between hubs and the fractal dimensions of the networks. First, statistics expression of inter-connections between any two hubs was defined and used to represent the connection pattern of the whole network. Our experimental results show that statistic values of inter-connections were obvious differences for each kind of complex model, and the more inter-connections, the more stable the network was. Secondly, the fractal dimension was considered to be a key factor related to vulnerability. Here we found the quantitative power function relationship between vulnerability and fractal dimension and gave the explicit mathematical formula. The results are helpful to build stable artificial network models through the analysis and comparison of the real brain network.

scholarly journals Test-retest reliability of functional brain network characteristics using resting-state EEG and graph theory

2018 ◽  
Author(s):  
Bahar Moezzi ◽  
Brenton Hordacre ◽  
Carolyn Berryman ◽  
Michael C. Ridding ◽  
Mitchell R. Goldsworthy
Keyword(s):  
Graph Theory ◽  
Resting State ◽  
Brain Network ◽  
Beta Band ◽  
Graph Metrics ◽  
Eeg Data ◽  
Source Level ◽  
Test Retest Reliability ◽  
High Beta ◽  
Healthy Participants

AbstractMetrics of brain network organization can be derived from neuroimaging data using graph theory. We explored the test-retest reliability of graph metrics of functional networks derived from resting-state electroencephalogram (EEG) recordings. Data were collected within two designs: (1) within sessions (WS) design where EEG data were collected from 18 healthy participants in four trials within a few hours and (2) between sessions (BS) design where EEG data were collected from 19 healthy participants in three trials on three different days at least one week apart. Electrophysiological source activity was reconstructed and functional connectivity between pairs of sensors or brain regions was determined in different frequency bands. We generated undirected binary graphs and used intra-class correlation coefficient (ICC) to estimate reliability. We showed that reliabilities ranged from poor to good. Reliability at the sensor-level was significantly higher than source-level. The most reliable graph metric at the sensor-level was cost efficiency and at the source-level was global efficiency. At the sensor-level: WS reliability was significantly higher than BS reliability; high beta band in WS design had the highest reliability; in WS design reliability in gamma band was significantly lower than reliability in low and high beta bands. At the source-level: low beta band in BS design had the highest reliability; there was no significant main effect of frequency band on reliability; reliabilities of WS and BS designs were not significantly different. These results suggest that these graph metrics can provide reliable outcomes, depending on how the data were collected and analysed.

scholarly journals Efficient small-world and scale-free functional brain networks at rest using k-nearest neighbors thresholding

2019 ◽  
Author(s):  
Caroline Garcia Forlim ◽  
Siavash Haghiri ◽  
Sandra Düzel ◽  
Simone Kühn
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Nearest Neighbors ◽  
Resting State Fmri ◽  
Brain Network ◽  
K Nearest Neighbors ◽  
Scale Free ◽  
The Brain ◽  
Density Threshold

AbstractIn recent years, there has been a massive effort to analyze the topological properties of brain networks. Yet, one of the challenging questions in the field is how to construct brain networks based on the connectivity values derived from neuroimaging methods. From a theoretical point of view, it is plausible that the brain would have evolved to minimize energetic costs of information processing, and therefore, maximizes efficiency as well as to redirect its function in an adaptive fashion, that is, resilience. A brain network with such features, when characterized using graph analysis, would present small-world and scale-free properties.In this paper, we focused on how the brain network is constructed by introducing and testing an alternative method: k-nearest neighbor (kNN). In addition, we compared the kNN method with one of the most common methods in neuroscience: namely the density threshold. We performed our analyses on functional connectivity matrices derived from resting state fMRI of a big imaging cohort (N=434) of young and older healthy participants. The topology of networks was characterized by the graph measures degree, characteristic path length, clustering coefficient and small world. In addition, we verified whether kNN produces scale-free networks. We showed that networks built by kNN presented advantages over traditional thresholding methods, namely greater values for small-world (linked to efficiency of networks) than those derived by means of density thresholds and moreover, it presented also scale-free properties (linked to the resilience of networks), where density threshold did not. A brain network with such properties would have advantages in terms of efficiency, rapid adaptive reconfiguration and resilience, features of brain networks that are relevant for plasticity and cognition as well as neurological diseases as stroke and dementia.HighlightsA novel thresholding method for brain networks based on k-nearest neighbors (kNN)kNN applied on resting state fMRI from a big cohort of healthy subjects BASE-IIkNN built networks present greater small world properties than density thresholdkNN built networks present scale-free properties whereas density threshold did not

scholarly journals Critical and Ictal Phases in Simulated EEG Signals on a Small-World Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Louis R. Nemzer ◽  
Gary D. Cravens ◽  
Robert M. Worth ◽  
Francis Motta ◽  
Andon Placzek ◽  
...  
Keyword(s):  
Simulated Data ◽  
Small World ◽  
Small World Network ◽  
Eeg Signals ◽  
Scale Free ◽  
Actual Patient ◽  
Artificial Neurons ◽  
Neuronal Network Dynamics ◽  
Critical Phases ◽  
Electroencephalogram Eeg

Healthy brain function is marked by neuronal network dynamics at or near the critical phase, which separates regimes of instability and stasis. A failure to remain at this critical point can lead to neurological disorders such as epilepsy, which is associated with pathological synchronization of neuronal oscillations. Using full Hodgkin-Huxley (HH) simulations on a Small-World Network, we are able to generate synthetic electroencephalogram (EEG) signals with intervals corresponding to seizure (ictal) or non-seizure (interictal) states that can occur based on the hyperexcitability of the artificial neurons and the strength and topology of the synaptic connections between them. These interictal simulations can be further classified into scale-free critical phases and disjoint subcritical exponential phases. By changing the HH parameters, we can model seizures due to a variety of causes, including traumatic brain injury (TBI), congenital channelopathies, and idiopathic etiologies, as well as the effects of anticonvulsant drugs. The results of this work may be used to help identify parameters from actual patient EEG or electrocorticographic (ECoG) data associated with ictogenesis, as well as generating simulated data for training machine-learning seizure prediction algorithms.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

STABILITY OF TWO TYPICAL COMPLEX DYNAMICAL NETWORKS

2008 ◽  
Vol 22 (05) ◽  
pp. 553-560 ◽  
Author(s):  
WU-JIE YUAN ◽  
XIAO-SHU LUO ◽  
PIN-QUN JIANG ◽  
BING-HONG WANG ◽  
JIN-QING FANG
Keyword(s):  
Numerical Simulation ◽  
Dissipative System ◽  
Small World ◽  
Complex Dynamical Networks ◽  
Dynamical Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
General Complex ◽  
The Stability ◽  
Theoretical Results

When being constructed, complex dynamical networks can lose stability in the sense of Lyapunov (i. s. L.) due to positive feedback. Thus, there is much important worthiness in the theory and applications of complex dynamical networks to study the stability. In this paper, according to dissipative system criteria, we give the stability condition in general complex dynamical networks, especially, in NW small-world and BA scale-free networks. The results of theoretical analysis and numerical simulation show that the stability i. s. L. depends on the maximal connectivity of the network. Finally, we show a numerical example to verify our theoretical results.

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