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Model of brain activation predicts the neural collective influence map of the brain

机译:大脑激活模型预测大脑的神经集体影响图

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摘要

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory.
机译:高效的复杂系统具有模块化结构,但是模块化不能保证鲁棒性,因为效率还要求交互模块组件之间的巧妙相互作用。人脑是作为网络网络(NoN)互连的高效健壮模块化系统的基本范例。从零件的互连中了解这种模块化体系结构中健壮性的出现是一个长期的挑战,已引起许多科学家的关注。当前受电网启发的NoN依赖关系模型通过易碎的联轴器来表达模块之间的相互作用,这种联轴器会放大甚至很小的冲击,从而妨碍功能。因此,我们引入了NoN模型来塑造大脑激活的模式,以形成强大的模块化环境。该模型通过优化负责将信息广播到全脑NoN的最小基本节点集的影响,来预测大脑中神经集体影响者(NCI)的分布图。我们的研究结果提出了干预方案,可通过针对网络理论预测的有影响力的神经节点来控制大脑活动。

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