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


Authors: Del Ferraro, G.; Moreno, A.; Min, B.; Morone, F.; Pérez-Ramírez, Ú; Pérez-Cervera, L.; Parra, L. C.; Holodny, A.; Canals, S.; Makse, H. A.
Article Title: 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. © 2018 The Author(s).
Keywords: nonhuman; animal experiment; in vivo study; rodent; rodentia; memory; nucleus accumbens; article
Journal Title: Nature Communications
Volume: 9
ISSN: 2041-1723
Publisher: Nature Publishing Group  
Date Published: 2018-06-11
Start Page: 2274
Language: English
DOI: 10.1038/s41467-018-04718-3
PROVIDER: scopus
PMCID: PMC5995874
PUBMED: 29891915
DOI/URL:
Notes: Erratum issued, see DOI: 10.1038/s41467-018-05686-4 -- Article -- Export Date: 2 July 2018 -- Source: Scopus
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  1. Andrei Holodny
    135 Holodny