TY - JOUR A1 - Zemanova, Lucia A1 - Zhou, Changsong A1 - Kurths, Jürgen T1 - Structural and functional clusters of complex brain networks JF - Physica, D, Nonlinear phenomena N2 - Recent research using the complex network approach has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. It is of importance to understand the implications of such complex network structures in the functional organization of the brain activities. Here we study this problem from the viewpoint of dynamical complex networks. We investigate synchronization dynamics on the corticocortical network of the cat by modeling each node (cortical area) of the network with a sub-network of interacting excitable neurons. We find that the network displays clustered synchronization behavior, and the dynamical clusters coincide with the topological community structures observed in the anatomical network. Our results provide insights into the relationship between the global organization and the functional specialization of the brain cortex. KW - cortical network KW - anatomical connectivity KW - functional connectivity KW - topological community KW - dynamical cluster Y1 - 2006 U6 - https://doi.org/10.1016/j.physd.2006.09.008 SN - 0167-2789 SN - 1872-8022 VL - 224 IS - 1-2 SP - 202 EP - 212 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Zhou, Changsong A1 - Zemanova, Lucia A1 - Zamora, Gorka A1 - Hilgetag, Claus C. A1 - Kurths, Jürgen T1 - Hierarchical organization unveiled by functional connectivity in complex brain networks JF - Physical review letters N2 - How do diverse dynamical patterns arise from the topology of complex networks? We study synchronization dynamics in the cortical brain network of the cat, which displays a hierarchically clustered organization, by modeling each node (cortical area) with a subnetwork of interacting excitable neurons. We find that in the biologically plausible regime the dynamics exhibits a hierarchical modular organization, in particular, revealing functional clusters coinciding with the anatomical communities at different scales. Our results provide insights into the relationship between network topology and functional organization of complex brain networks. Y1 - 2006 U6 - https://doi.org/10.1103/PhysRevLett.97.238103 SN - 0031-9007 SN - 1079-7114 VL - 97 PB - American Physical Society CY - College Park ER - TY - JOUR A1 - Zhou, Changsong A1 - Kurths, Jürgen T1 - Hierarchical synchronization in complex networks with heterogeneous degrees N2 - We study synchronization behavior in networks of coupled chaotic oscillators with heterogeneous connection degrees. Our focus is on regimes away from the complete synchronization state, when the coupling is not strong enough, when the oscillators are under the influence of noise or when the oscillators are nonidentical. We have found a hierarchical organization of the synchronization behavior with respect to the collective dynamics of the network. Oscillators with more connections (hubs) are synchronized more closely by the collective dynamics and constitute the dynamical core of the network. The numerical observation of this hierarchical synchronization is supported with an analysis based on a mean field approximation and the master stability function. (C) 2006 American Institute of Physics Y1 - 2006 UR - http://scitation.aip.org/getpdf/servlet/ GetPDFServlet?filetype=pdf&id=CHAOEH000016000001015104000001&idtype=cvips&doi=10.1063/1.2150381&prog=normal U6 - https://doi.org/10.1063/1.2150381 SN - 1054-1500 ER - TY - JOUR A1 - Zhou, Changsong A1 - Motter, Adilson E. A1 - Kurths, Jürgen T1 - Universality in the synchronization of weighted random networks N2 - Realistic networks display not only a complex topological structure, but also a heterogeneous distribution of weights in the connection strengths. Here we study synchronization in weighted complex networks and show that the synchronizability of random networks with a large minimum degree is determined by two leading parameters: the mean degree and the heterogeneity of the distribution of node's intensity, where the intensity of a node, defined as the total strength of input connections, is a natural combination of topology and weights. Our results provide a possibility for the control of synchronization in complex networks by the manipulation of a few parameters Y1 - 2006 UR - http://prl.aps.org/pdf/PRL/v96/i3/e034101 U6 - https://doi.org/10.1103/Physrevlett.96.034101 ER - TY - JOUR A1 - Zhou, Changsong A1 - Kurths, Jürgen T1 - Dynamical weights and enhanced synchronization in adaptive complex networks N2 - Dynamical organization of connection weights is studied in scale-free networks of chaotic oscillators, where the coupling strength of a node from its neighbors develops adaptively according to the local synchronization property between the node and its neighbors. We find that when complete synchronization is achieved, the coupling strength becomes weighted and correlated with the topology due to a hierarchical transition to synchronization in heterogeneous networks. Importantly, such an adaptive process enhances significantly the synchronizability of the networks, which could have meaningful implications in the manipulation of dynamical networks Y1 - 2006 UR - http://link.aps.org/doi/10.1103/PhysRevLett.96.164102 U6 - https://doi.org/10.1103/Physrevlett.96.164102 ER - TY - JOUR A1 - Baptista, Murilo da Silva A1 - Zhou, Changsong A1 - Kurths, Jürgen T1 - Information transmission in phase synchronous chaotic arrays N2 - We show many versatile phase synchronous configurations that emerge in an array of coupled chaotic elements due to the presence of a periodic stimulus. Then, we explain the relevance of these configurations to the understanding of how information about such a. stimulus is transmitted from one side to the other in this array. The stimulus actively creates the ways to be transmitted, by making the chaotic elements to phase synchronize Y1 - 2006 UR - http://iopscience.iop.org/0256-307X/ U6 - https://doi.org/10.1088/0256-307X/23/3/010 SN - 0256-307X ER -