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

机译:使用最佳渗流理论寻找在脑网络中整合的影响节点

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摘要

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.
机译:大脑中信息的全球整合是由分离的大脑网络之间复杂的相互作用造成的。识别有效结合这些网络的最具影响力的神经元群体是系统神经科学的基本问题。在这里,我们在体内应用最佳渗滤理论和药物遗传学干预措施,以预测并随后靶向对啮齿类动物的记忆网络进行全球整合至关重要的节点。该理论预测,存储网络中的整合是由伏伏核中的一组低度节点介导的。伏伏核的药物遗传学失活证实了这一结果,它消除了记忆网络的形成,而其他大脑区域的失活则使网络完整。因此,最佳渗流理论可预测大脑网络中的基本节点。这可以用于确定调节脑功能的干预目标。

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