Search Results for « Network Connectivity Analysis »

Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

An exponential-type approximation of the first order Marcum Q-function is presented, which is robust to changes in its first argument and can easily be integrated with respect to the second argument. Such characteristics are particularly useful in network connectivity analysis. The proposed approximation is exact in the limit of small first argument of the Marcum Q-function, in which case the optimal parameters can be obtained analytically. For larger values of the first argument, an optimization problem is solved, and the parameters can be accurately represented using regression analysis. Numerical results indicate that the proposed methods result in approximations very close to the actual Marcum Q-function for small and moderate values of the first argument. We demonstrate the accuracy of the approximation by using it to analyze the connectivity properties of random ad hoc networks operating in a Rician fading environment.

Recent research has demonstrated the importance of boundary effects on the overall connection probability of wireless networks, but has largely focused on convex domains. We consider two generic scenarios of practical importance to wireless communications, in which one or more nodes are located outside the convex space where the remaining nodes reside. Consequently, conventional approaches with the underlying assumption that only line-of-sight (LOS) or direct connections between nodes are possible, fail to provide the correct analysis for the connectivity. We present an analytical framework that explicitly considers the effects of reflections from the system boundaries on the full connection probability. This study provides a different strategy to ray tracing tools for predicting the wireless propagation environment. A simple two-dimensional geometry is first considered, followed by a more practical three-dimensional system. We investigate the effects of different system parameters on the connectivity of the network though analysis corroborated by numerical simulations, and highlight the potential of our approach for more general non-convex geometries.t system parameters on the connectivity of the network through simulation and analysis.

Abstract In network structure analysis, metrics such as Isolated Node Ratio (INR), Network Efficiency (NE), Network Clustering Coefficient (NCC), Betweenness Centrality (BC), and Closeness Centrality (CC) are used as quantitative tools to measure network connectivity. However, there is another metric that is often easily overlooked and underestimated, i.e., the Relative Size of Largest Connected Component (RSLCC), we do not find any literature that analyzed RSLCC in a separate study. However, through the research in this paper, we not only prove that this metric is underestimated, but also design 7 methods to predict the value of this metric, with a Deep Neural Network (DNN) prediction accuracy of more than 99%. This research results can be applied to any network, and in a disaster scenario, whether it is a physical entity network or a virtual abstract network, the approximate network connectivity value can be predicted simply by knowing the number of connected edges in the pre-disaster network and the number of connected edges in the post-disaster network.

Objectives Accurate localization of the epileptogenic zones (EZs) is essential for the successful surgical treatment of the refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG) signals is effective in localizing the EZs. Methods SEEG data were recorded from seven patients underwent presurgical evaluation for the treatment of refractory focal epilepsy, and the subsequent resective surgery gave the patients good outcome. The time-variant multivariate autoregressive model was constructed by Kalman filter and the time-variant partial directed coherence was computed, which was then used to construct the dynamic directed network of the epileptic brain. Three graph measures, in-degree, out-degree and betweenness centrality, were used to analyze the characteristics of the dynamic network and to find the important nodes in it. Results In all seven patients, the indicative EZs localized by in-degree and betweenness centrality were highly consistent to the clinical diagnosed EZs. However, the out-degree did not indicate significant difference between nodes in the network. Conclusions In this work, the method based on ictal SEEG signals and effective connectivity analysis localized the EZs accurately. It suggested that in-degree and betweenness centrality may be better network characteristics to localize the EZs than out-degree.

Abstract Complex systems of interacting components often can be modeled by a simple graph $$\mathcal {G}$$ that consists of a set of n nodes and a set of m edges. Such a graph can be represented by an adjacency matrix $$A\in \mathbb {R}^{n\times n}$$ , whose ( ij )th entry is one if there is an edge pointing from node i to node j , and is zero otherwise. The matrix A and its positive integer powers reveal important properties of the graph and allow the construction of the path length matrix L for the graph. The ( ij )th entry of L is the length of the shortest path from node i to node j ; if there is no path between these nodes, then the value of the entry is set to $$\infty$$ . We are interested in how well information flows via shortest paths of the graph. This can be studied with the aid of the path length matrix. The path length matrix allows the definition of several measures of communication in the network defined by the graph such as the global K -efficiency, which considers shortest paths that are made up of at most K edges for some $$K , as well as the number of such shortest paths. Novel notions of connectivity introduced in this paper help us understand the importance of specific edges for the flow of information through the graph. This is of interest when seeking to simplify a network by removing selected edges or trying to assess the sensitivity of the flow of information to changes due to exterior causes such as a traffic stoppage on a road network.