Finding influential nodes for integration in brain networks using optimal percolation theory

Abstract

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here we apply optimal percolation theory and pharmacogenetic interventions in-vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

20 pages, 6 figures, Supplementary Info

References in corpus (12)

• Community structure in social and biological networks
• Community detection in graphs
• Error and attack tolerance of complex networks
• Functional cartography of complex metabolic networks
• Random graphs with arbitrary degree distributions and their applications
• Identification of influential spreaders in complex networks
• Influence maximization in complex networks through optimal percolation
• A small-world of weak ties provides optimal global integration of self-similar modules in functional brain networks
• Localization and centrality in networks
• Avoiding catastrophic failure in correlated networks of networks
• A topological criterion for filtering information in complex brain networks
• Model of Brain Activation Predicts the Neural Collective Influence Map of the Brain

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