Figures
Filtering information in brain networks
Complex brain networks are mainly estimated from empirical measurements. As a result, filtering procedures are typically adopted to prune the weakest connections. The structural properties of the thresholded networks depend on the number of remaining links and how to objectively fix such threshold is still an open issue. We propose a possible criterion to filter connectivity based on the optimization of fundamental properties in complex systems, such as efficiency and economy, and we show that a general law can be derived. Given its generality, ECO can advance the ability to analyze biological networks inferred from experimentally obtained data. De Vico Fallani et al.
Image Credit: Fabrizio De Vico Fallani
Citation: (2017) PLoS Computational Biology Issue Image | Vol. 13(1) January 2017. PLoS Comput Biol 13(1): ev13.i01. https://doi.org/10.1371/image.pcbi.v13.i01
Published: January 31, 2017
Copyright: © 2017 De Vico Fallani. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Complex brain networks are mainly estimated from empirical measurements. As a result, filtering procedures are typically adopted to prune the weakest connections. The structural properties of the thresholded networks depend on the number of remaining links and how to objectively fix such threshold is still an open issue. We propose a possible criterion to filter connectivity based on the optimization of fundamental properties in complex systems, such as efficiency and economy, and we show that a general law can be derived. Given its generality, ECO can advance the ability to analyze biological networks inferred from experimentally obtained data. De Vico Fallani et al.
Image Credit: Fabrizio De Vico Fallani