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

TL;DR

This study combines optimal percolation theory with in-vivo pharmacogenetic interventions to identify and validate key low-degree nodes in brain networks that are crucial for global information integration, specifically in memory networks of rodents.

Contribution

It demonstrates that optimal percolation theory can accurately predict influential nodes in brain networks, validated through targeted pharmacogenetic inactivation.

Findings

Nucleus accumbens is essential for memory network integration.

Inactivation of nucleus accumbens disrupts the memory network.

Other brain areas' inactivation does not affect the network.

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.