Statistical characterization of an ensemble of functional neural networks

Abstract

This work uses a complex network approach to analyze temporal sequences of electrophysiological signals of brain activity from freely behaving rats. A network node represents a neuron and a network link is included between a pair of nodes whenever their firing rates are correlated. The framework of time varying graph (TVG) is used to deal with a very large number (>30 000) of time dependent networks, which are set up by taking into account correlations between neuron firing rates in a moving time lag window of suitable width. Statistical distributions for the following network measures are obtained: size of the largest connected cluster, number of edges, average node degree, and average minimal path. We find that the number of networks with highly correlated activity in distinct brain areas has a fat-tailed distribution, irrespective of the behavioral state of the animal. This contrasts with short-tailed distributions for surrogates obtained by shuffling the original data, and reflects the fact that neurons in the neocortex and hippocampus often act in precise temporal coordination. Our results also suggest that functional neuronal networks at the millimeter scale undergo statistically nontrivial rearrangements over time, thus delimitating an empirical constraint for models of brain activity.

Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

“…In conclusion, our findings revealed that Motif-Synchronization method could be a feasible tool for characterizing the dynamic evolution of functional brain networks [11,12,26]. Previous research on fMRI has already revealed relevant characteristics of the dynamic of these functional networks [27,28].…”

“…In the first analysis, the ability of our MS method to detect driver response relationships was tested by using simulated data from unidirectionally coupled model systems. In the second analysis, the feasibility of our MS method to compute functional connectivity on real EEG data was compared with two other methods: Event-Synchronization [19] and Pearson correlation [11]. In the third analysis, the hub distribution of the functional networks was further examined by comparing EEG datasets from two subgroups of participants.…”

“…In this section, our MS method was computed on real EEG data and results were compared to those obtained from two already known methods for functional connectivity analyses: Event-Synchronization [19] and Pearson correlation [11].…”

“…Recently, it has been proposed that Time-Varying Graphs (TVG) may provide a unified framework for a detailed analysis of both static and dynamic characteristics of complex networks [9,10]. Thus, for instance, TVG formulations have been used to analyze the dynamic of functional neuronal networks from freely behaving rats [11] and functional connectivity from human EEG data [12]. The major aim of the present work was to analyze the feasibility of a new method for the study of functional brain connectivity called Motif-Synchronization and based on TVG formalism.…”

Coupled variability in primary sensory areas and the hippocampus during spontaneous activity

“…The stereotaxic coordinates for craniotomy and probe insertion in V1 combined information from Paxinos Atlas44 and recent studies30454647. The 64-channel silicon probe was then inserted around the central coordinate along the direction defined by the AP axis.…”