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Nat Commun. 2018 Jun 11;9(1):2274. doi: 10.1038/s41467-018-04718-3.

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

Author information

1
Levich Institute and Physics Department, City College of New York, New York, NY, 10031, USA.
2
Instituto de Neurociencias, CSIC and UMH, 03550, San Juan de Alicante, Spain.
3
Department of Physics, Chungbuk National University, Cheongju, Chungbuk, 28644, Korea.
4
Center for Biomaterials and Tissue Engineering, UPV, Valencia, Spain.
5
Biomedical Engineering, City College of New York, New York, NY, 10031, USA.
6
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
7
Instituto de Neurociencias, CSIC and UMH, 03550, San Juan de Alicante, Spain. scanals@umh.es.
8
Levich Institute and Physics Department, City College of New York, New York, NY, 10031, USA. hmakse@lev.ccny.cuny.edu.

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.

PMID:
29891915
PMCID:
PMC5995874
DOI:
10.1038/s41467-018-04718-3
[Indexed for MEDLINE]
Free PMC Article

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