Objective: To investigate the changes in EEG connectivity in children with the typical presentation of benign epilepsy with centro-temporal spikes (BECTS).
Methods: We compared awake and spindle-sleep EEG recordings obtained by a standard electrode array in patients with lateralised (10 Right, 9 Left-BECTS) or bilateral spikes (10 MF-BECTS) and in 17 age-matched controls. We analysed EEG activity using partial directed coherence, an estimator of connectivity based on the multivariate autoregressive models and calculated in- and out-degrees, strength, clustering coefficient and betweenness centrality.
Results: In comparison with the controls, the awake EEG recordings of the patients with lateralised BECTS showed a minimal increase in out-degrees on F4 and F3. The greater differences, found during sleep, included significant reductions in both in- and out-degrees and strength in all of the patient groups, but in T4 or T3 showing increased out-degrees and strength in Right and Left-BECTS. Betweenness centrality was significantly reduced on C3 and C4 in the patients with MF-BECTS.
Conclusions: Our observations suggest that the main finding in BECTS patients is widely reduced local connectivity.
Significance: The network changes in BECTS can be interpreted as a permissive condition occurring in a developmental window that predisposes to seizure generation during spindle-sleep.
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http://dx.doi.org/10.1016/j.clinph.2018.09.008 | DOI Listing |
JMIR Public Health Surveill
November 2020
School of Statistics, Capital University of Economics and Business, Beijing, China.
Background: Since the outbreak of COVID-19 in December 2019 in Wuhan, Hubei Province, China, frequent interregional contacts and the high rate of infection spread have catalyzed the formation of an epidemic network.
Objective: The aim of this study was to identify influential nodes and highlight the hidden structural properties of the COVID-19 epidemic network, which we believe is central to prevention and control of the epidemic.
Methods: We first constructed a network of the COVID-19 epidemic among 31 provinces in mainland China; after some basic characteristics were revealed by the degree distribution, the k-core decomposition method was employed to provide static and dynamic evidence to determine the influential nodes and hierarchical structure.
Biol Cybern
June 2020
School of Natural and Computational Sciences, Massey University, NSMC, Private Bag 102-904, Auckland, New Zealand.
We consider the effects of correlations between the in- and out-degrees of individual neurons on the dynamics of a network of neurons. By using theta neurons, we can derive a set of coupled differential equations for the expected dynamics of neurons with the same in-degree. A Gaussian copula is used to introduce correlations between a neuron's in- and out-degree, and numerical bifurcation analysis is used determine the effects of these correlations on the network's dynamics.
View Article and Find Full Text PDFClin Neurophysiol
November 2018
Department of Neurophysiopathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milano, Italy.
Objective: To investigate the changes in EEG connectivity in children with the typical presentation of benign epilepsy with centro-temporal spikes (BECTS).
Methods: We compared awake and spindle-sleep EEG recordings obtained by a standard electrode array in patients with lateralised (10 Right, 9 Left-BECTS) or bilateral spikes (10 MF-BECTS) and in 17 age-matched controls. We analysed EEG activity using partial directed coherence, an estimator of connectivity based on the multivariate autoregressive models and calculated in- and out-degrees, strength, clustering coefficient and betweenness centrality.
Neural Netw
July 2016
Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 705-115, Republic of Korea. Electronic address:
We investigate the effect of network architecture on burst and spike synchronization in a directed scale-free network (SFN) of bursting neurons, evolved via two independent α- and β-processes. The α-process corresponds to a directed version of the Barabási-Albert SFN model with growth and preferential attachment, while for the β-process only preferential attachments between pre-existing nodes are made without addition of new nodes. We first consider the "pure" α-process of symmetric preferential attachment (with the same in- and out-degrees), and study emergence of burst and spike synchronization by varying the coupling strength J and the noise intensity D for a fixed attachment degree.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
February 2016
Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115
Increasing evidence shows that real-world systems interact with one another via dependency connectivities. Failing connectivities are the mechanism behind the breakdown of interacting complex systems, e.g.
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