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

Correction to: Nature Communications https://doi.org/10.1038/s41467-018-04718-3; published online: 11 June 2018.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.This work was supported by NIH-NIBIB 1R01EB022720, NSF IIS-1515022, NIH-NCI U54CA137788/U54CA132378, NSF PHY-1305476, NIH-NINDS R01 NS095123, and by MINECO and FEDER Grants BFU2015-64380-C2-1-R, EU Horizon 2020 Grant No. 668863 (SyBil-AA), and Spanish State Research Agency, through the “Severo Ochoa” Program for Centers of Excellence in R&D (ref. SEV-2013-0317). Ú.P.-R. was supported by MECD Grant FPU13/03537.Peer reviewe