Résultats de recherche pour « Network Connectivity Analysis »

The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.

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.

Assessing centrality in network systems is critical for understanding node importance and guiding decision-making processes. In dynamic networks, incorporating a controllability perspective is essential for identifying key nodes. In this paper, we study two control theoretic centrality measures -- the Volumetric Controllability Score (VCS) and Average Energy Controllability Score (AECS) -- to quantify node importance in linear time-invariant network systems. We prove the uniqueness of VCS and AECS for almost all specified terminal times, thereby enhancing their applicability beyond previously recognized cases. This ensures their interpretability, comparability, and reproducibility. Our analysis reveals substantial differences between VCS and AECS in linear systems with symmetric and skew-symmetric transition matrices. We also investigate the dependence of VCS and AECS on the terminal time and prove that when this parameter is extremely small, both scores become essentially uniform. Additionally, we prove that a sequence generated by a projected gradient method for computing VCS and AECS converges linearly to both measures under several assumptions. Finally, evaluations on brain networks modeled via Laplacian dynamics using real data reveal contrasting evaluation tendencies and correlations for VCS and AECS, with AECS favoring brain regions associated with cognitive and motor functions, while VCS emphasizes sensory and emotional regions.

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.

Understanding the human brain remains the Holy Grail in biomedical science, and arguably in all of the sciences. Our brains represent the most complex systems in the world (and some contend the universe) comprising nearly one hundred billion neurons with septillions of possible connections between them. The structure of these connections engenders an efficient hierarchical system capable of consciousness, as well as complex thoughts, feelings, and behaviors. Brain connectivity and network analyses have exploded over the last decade due to their potential in helping us understand both normal and abnormal brain function. Functional connectivity (FC) analysis examines functional associations between time series pairs in specified brain voxels or regions. Brain network analysis serves as a distinct subfield of connectivity analysis in which associations are quantified for all time series pairs to create an interconnected representation of the brain (a brain network), which allows studying its systemic properties. While connectivity analyses underlie network analyses, the subtle distinction between the two research areas has generally been overlooked in the literature, with them often being referred to synonymously. However, developing more useful analytic methods and allowing for more precise biological interpretations requires distinguishing these two complementary domains.

Advances in simultaneous recordings of large numbers of neurons have driven significant interest in the structure of neural population activity, such as dimensionality, and in how it arises mechanistically from network connectivity. The covariance eigenvalue distribution, or spectrum, which can be derived analytically in random recurrent networks, was proposed as a robust description of population geometry beyond dimensionality. The theoretical spectrum has since matched data across brain areas and species, serving as a computational biomarker linking neural population activity to network connectivity. However, this empirical success highlights a gap in theory, as the model used to derive the spectrum was minimal with linear neurons. Here we close this gap by studying the covariance spectrum in networks with nonlinear neurons and across broader dynamical regimes, including chaos. Surprisingly, the spectrum is precisely described by equations analogous to the linear theory, substituted with an effective recurrent connection strength, which reflects both the connection weights and the dynamic gain of neurons. Across regimes, this effective parameter gives a unified account of the spectrum and dimensionality changes, and stays near its critical value throughout the chaotic regime, suggesting a mechanism for brain criticality without fine-tuning and a new interpretation for previously observed near-critical spectra. Our key step is proposing a covariance matrix ansatz under nonlinear dynamics, which explicitly links neuronal dynamics, recurrent connectivity, and all pairwise correlations in the network. The ansatz is supported by extensive numerical and theoretical validations, including extensions to other network models. These results advance the theory of nonlinear population dynamics and strength the covariance spectrum analysis a practical tool for biological circuits.

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.

Analysing correlations between streams of events is an important problem. It arises for example in Neurosciences, when the connectivity of neurons should be inferred from spike trains that record neurons' individual spiking activity. While recently some approaches for inferring delayed synaptic connections have been proposed, they are limited in the types of connectivities and delays they are able to handle, or require computation-intensive procedures. This paper proposes a faster and more flexible approach for analysing such delayed correlated activity: a statistical approach for the Analysis of Connectivity in spiking Events (ACE), based on the idea of hypothesis testing. It first computes for any pair of a source and a target neuron the inter-spike delays between subsequent source- and target-spikes. Then, it derives a null model for the distribution of inter-spike delays for \emph{uncorrelated}~neurons. Finally, it compares the observed distribution of inter-spike delays to this null model and infers pairwise connectivity based on the Pearson's Chi-squared test statistic. Thus, ACE is capable to detect connections with a priori unknown, non-discrete (and potentially large) inter-spike delays, which might vary between pairs of neurons. Since ACE works incrementally, it has potential for being used in online processing. In our experiments, we visualise the advantages of ACE in varying experimental scenarios (except for one special case) and in a state-of-the-art dataset which has been generated for neuro-scientific research under most realistic conditions.

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.