13,996 research outputs found
Characterization of the Community Structure of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction
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
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
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
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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