title: A statistical model for brain networks inferred from large-scale electrophysiological signals.
abstract: Network science has been extensively developed to characterize the structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graph models (ERGMs) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset ofN= 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes than other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in real brain networks, with EO eliciting significantly higher segregation in thealphafrequency band (8-13 Hz) than EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in thealpharange, but also in thebetaband (14-29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the functional segregation and integration of the brain in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.
authors.affiliation: Inria Paris, Aramis Project Team, 75013 Paris, France.|Sorbonne Universites, UPMC Univ. Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), Hôpital Pitié-Salpêtrière, 75013 Paris, France.
authors.forename: Catalina
authors.initials: C
authors.lastname: Obando
authors.affiliation: Inria Paris, Aramis Project Team, 75013 Paris, France fabrizio.devicofallani@gmail.com.|Sorbonne Universites, UPMC Univ. Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), Hôpital Pitié-Salpêtrière, 75013 Paris, France.
authors.forename: Fabrizio
authors.identifier: 0000-0001-8035-7883
authors.initials: F
authors.lastname: De Vico Fallani
mesh_terms: D001921:Brain / Q000502:physiology*; D058256:Brain Waves / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*
keywords: brain connectivity; electroencephalography; exponential random graph models; graph theory; resting states