title: A Topological Criterion for Filtering Information in Complex Brain Networks.
abstract: In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.
authors.affiliation: Inria Paris, Aramis project-team, Paris, France.|CNRS UMR-7225, Sorbonne Universités, UPMC Univ Paris 06, Inserm, Institut du cerveau et de la moelle (ICM) - Hôpital Pitié-Salpêtrière, Paris, France.
authors.forename: Fabrizio
authors.identifier: 0000-0001-8035-7883
authors.initials: F
authors.lastname: De Vico Fallani
authors.affiliation: School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom.|Dipartimento di Fisica e Astronomia, Università di Catania and INFN, Catania, Italy.
authors.forename: Vito
authors.initials: V
authors.lastname: Latora
authors.affiliation: CNRS UMR-7225, Sorbonne Universités, UPMC Univ Paris 06, Inserm, Institut du cerveau et de la moelle (ICM) - Hôpital Pitié-Salpêtrière, Paris, France.
authors.forename: Mario
authors.initials: M
authors.lastname: Chavez
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000502:physiology*