13,996 research outputs found

    Characterization of the Community Structure of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction

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    One of the central questions in neuroscience is to understand the way communication is organized in the brain, trying to comprehend how cognitive capacities or physiological states of the organism are potentially related to brain activities involving interactions of several brain areas. One important characteristic of the functional brain networks is that they are modularly structured, being this modular architecture regarded to account for a series of properties and functional dynamics. In the neurobiological context, communities may indicate brain regions that are involved in one same activity, representing neural segregated processes. Several studies have demonstrated the modular character of organization of brain activities. However, empirical evidences regarding to its dynamics and relation to different levels of consciousness have not been reported yet. Within this context, this research sought to characterize the community structure of functional brain networks during an anesthetic induction process. The experiment was based on intra-cranial recordings of neural activities of an old world macaque of the species Macaca fuscata during a Ketamine-Medetomidine anesthetic induction process. Networks were serially estimated in time intervals of five seconds. Changes were observed within about one and a half minutes after the administration of the anesthetics, revealing the occurrence of a transition on the community structure. The awake state was characterized by the presence of large clusters involving frontal and parietal regions, while the anesthetized state by the presence of communities in the primary visual and motor cortices, being the areas of the secondary associative cortex most affected. The results report the influence of general anesthesia on the structure of functional clusters, contributing for understanding some new aspects of neural correlates of consciousness.Comment: 24 pages, 8 figures. arXiv admin note: text overlap with arXiv:1604.0000

    Model-free reconstruction of neuronal network connectivity from calcium imaging signals

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    A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically unfeasible even in dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct approximations to network structural connectivities from network activity monitored through calcium fluorescence imaging. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time-series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the effective network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (e.g., bursting or non-bursting). We thus demonstrate how conditioning with respect to the global mean activity improves the performance of our method. [...] Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good reconstruction of the network clustering coefficient, allowing to discriminate between weakly or strongly clustered topologies, whereas on the other hand an approach based on cross-correlations would invariantly detect artificially high levels of clustering. Finally, we present the applicability of our method to real recordings of in vitro cortical cultures. We demonstrate that these networks are characterized by an elevated level of clustering compared to a random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted for publicatio

    Graph analysis of functional brain networks: practical issues in translational neuroscience

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    The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

    Characterizing time series : when Granger causality triggers complex networks

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    In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH* human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length
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