Structure–function relationship in complex brain networks expressed by hierarchical synchronization

By Changsong Zhou, L. Zemanova, G. Zamora-López, Claus C. Hilgetag, Jürgen Kurths. 2007

170 citations recorded by the source corpus.

The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

Subfields: Cognitive Neuroscience, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/77679a36314eb0a201b3172422363a8862695de5

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In Cognitive Neuroscience

In Computer Networks and Communications

Citations in the corpus

Synchronization in complex networks (2008, background): “In a series of papers (Zemanová et al., 2006; Zhou, Zemanová, Zamora, Hilgetag and Kurths, 2006; Zhou et al., 2007) the dynamics of a realistic cortico-cortical projection network of the cat has been modeled at the level of functional areas (Scannell et al., 1999).”
Multilayer networks (2013, background): “In fact, a perspective based on a network of networks — where oscillators in a population are coupled to each other via some network structure and then these populations are in turn coupled to each other in a network — was used several years ago (before networks of networks became popular), with an ”
Recurrence-based time series analysis by means of complex network methods (2010, background): “…[Freeman, 1979], the assessment of functional connectivity in the brain from spatially distributed (multi-channel) neurophysiological measurements [Zhou et al. , 2006, 2007], or the identiﬁcation of dynamically relevant backbone structures in complex network representations of continuous systems…”
Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease (2012, background): “It has been shown that macroscopic models of mammalian brain networks combined with graph theoretical analysis may explain the topology of functional networks at various time scales [34–36].”