Finding influential nodes for integration in brain networks using optimal percolation theory.
Finding influential nodes for integration in brain networks using optimal percolation theory.
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DOI：
10.1038/s41467-018-04718-3
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发表时间：
2018-06-11
影响因子：
16.6
通讯作者：
Makse HA
中科院分区：
文献类型：
--
作者：
Del Ferraro G;Moreno A;Min B;Morone F;Pérez-Ramírez Ú;Pérez-Cervera L;Parra LC;Holodny A;Canals S;Makse HA
大脑中信息的全球整合是分离的大脑网络复杂相互作用的结果。识别有效地绑定这些网络的最有影响力的神经元群体是系统神经科学的一个基本问题。在这里，我们应用最优渗流理论和体内的药物遗传学干预来预测并随后针对啮齿动物记忆网络的全球整合所必需的节点。该理论预测，记忆网络中的整合是由位于伏隔核的一组低度节点介导的。这一结果得到了伏隔核药物失活的证实，伏隔核的失活消除了记忆网络的形成，而其他大脑区域的失活则使网络保持不变。因此，最优逾渗理论预测了大脑网络中的关键节点。这可以用来确定干预的目标，以调节大脑功能。复杂网络可以用来模拟大脑网络。在这里，作者确定了大脑网络模型中的关键节点，然后通过体内药物遗传学干预来验证这些预测。他们发现伏隔核是大脑整合的中心区域。
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. Complex networks can be used to model brain networks. Here the authors identify the essential nodes in a model of a brain network and then validate these predictions by means of in vivo pharmacogenetic interventions. They find that the nucleus accumbens is a central region for brain integration.
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影响因子：
4.6
作者：
De Asis-Cruz J;Bouyssi-Kobar M;Evangelou I;Vezina G;Limperopoulos C
通讯作者：
Limperopoulos C
影响因子：
64.8
作者：
Albert, R;Jeong, H;Barabási, AL
通讯作者：
Barabási, AL
DOI：
10.1073/pnas.1208933109
发表时间：
2012-12-11
影响因子：
11.1
作者：
Achard, Sophie;Delon-Martin, Chantal;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
DOI：
10.1073/pnas.1502052112
发表时间：
2015-07-21
影响因子：
11.1
作者：
Lo, Chun-Yi Zac;Su, Tsung-Wei;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
4.3
作者：
Joyce KE;Hayasaka S;Laurienti PJ
通讯作者：
Laurienti PJ