1_Complex networks new trends for the analysis of brain connectivity

1_Complex networks new trends for the analysis of brain connectivity

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Unformatted text preview: Complex networks: new trends for the analysis of brain connectivity MARIO CHAVEZ LENA-CNRS UPR-640. Hˆ opital de la Salpˆ etri` ere. 47 Bd. de l’Hˆ opital, 75651 Paris CEDEX 13, France MIGUEL VALENCIA LENA-CNRS UPR-640. Hˆ opital de la Salpˆ etri` ere. 47 Bd. de l’Hˆ opital, 75651 Paris CEDEX 13, France Department of Neurological Sciences, Center of Applied Medical Research, University of Navarra, Avda Pio XII 31. 31008, Pamplona, Navarra VITO LATORA Dipartimento di Fisica e Astronomia, Universit` a di Catania and INFN, Via S. Sofia, 64, 95123 Catania, Italy Laboratorio sui Sistemi Complessi, Scuola Superiore di Catania, Via San Nullo 5/i, 95123 Catania, Italy JACQUES MARTINERIE LENA-CNRS UPR-640. Hˆ opital de la Salpˆ etri` ere. 47 Bd. de l’Hˆ opital, 75651 Paris CEDEX 13, France Last revised version: February 3, 2010 Abstract Today, the human brain can be studied as a whole. Electroencephalography, magnetoen- cephalography, or functional magnetic resonance imaging (fMRI) techniques provide functional connectivity patterns between different brain ar- eas, and during different pathological and cog- nitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently man- age local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy chang- ing neural demands. 1 Introduction In recent years, complex networks have provided an increasingly challenging framework for the study of collective behaviors in complex systems, based on the interplay between the wiring ar- chitecture and the dynamical properties of the coupled units [1, 2]. Many real networks were found to exhibit small-world features. Small- world (SW) networks are characterized by hav- 1 arXiv:1002.0697v1 [physics.data-an] 3 Feb 2010 ing a small average distance between any two nodes, as random graphs, and a high clustering coefficient, as regular lattices [3, 4, 5, 6]. Thus, a SW architecture is an attractive model for brain connectivity because it leads distributed neural assemblies to be integrated into a coherent pro- cess with an optimized wiring cost [7, 8, 9]. Another property observed in many networks is the existence of a modular organization in the wiring structure. Examples range from RNA structures, to biological organisms and social groups. A module is currently defined as a subset of units within a network such that connections between them are denser than connections with the rest of the network. It is generally acknowl- edged that modularity increases robustness, flex- ibility and stability of biological systems [10, 11]. The widespread character of modular architec- ture in real-world networks suggests that a net- work’s function is strongly ruled by the organi- zation of their structural subgroups....
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This note was uploaded on 11/08/2011 for the course CS 11003 taught by Professor Hongweizhao during the Winter '11 term at Tianjin University.

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1_Complex networks new trends for the analysis of brain connectivity

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