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Abstract：
A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their \"controllability\", drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain networks provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks. We then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations. We find that brain networks are not controllable (in a statistical significant way) by one single region. Additionally, we show that random null models, with no biological resemblance to brain network architecture, produce the same type of relationship observed by Gu et al. between the average/modal controllability and weighted degree. Finally, we find that resting state networks defined with fMRI cannot be attributed specific control roles. In summary, our study highlights some warning and caveats in the brain controllability framework.
摘要：
Gu等人最近的一篇文章。（纳特。Commun.6，2015)提出来表征大脑网络，使用解剖扩散成像量化，就他们的“可控性”而言，借鉴控制理论的概念和方法。他们报告说，大脑活动可以从一个节点控制，并且大脑网络的拓扑结构为不同区域在大脑中扮演的控制角色的类型提供了解释。在这项工作中,我们首先简要回顾了应用于复杂网络的控制理论框架。然后，我们通过对五个不同数据集和数值模拟的分析，展示了大脑可控性的对比结果。我们发现，大脑网络不是由一个单一区域控制的（以统计意义的方式）。此外,我们证明了随机零模型，与大脑网络结构没有生物学上的相似之处，产生与Gu等人观察到的相同类型的关系。在平均/模态可控性和加权度之间。最后,我们发现用fMRI定义的静息状态网络不能归因于特定的控制角色.总之,我们的研究强调了大脑可控性框架中的一些警告和警告。
