Lee Byeongwook, Kang Uiryong, Chang Hongjun, Cho Kwang-Hyun
Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
iScience. 2019 Mar 29;13:154-162. doi: 10.1016/j.isci.2019.02.017. Epub 2019 Feb 20.
The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency.
大脑以强大且高效的方式控制各种认知功能。能够实现这种强大且最优控制的脑网络控制架构是怎样的?这种大脑控制架构与其他复杂网络的控制架构有区别吗?在此,我们开发了一个框架来描绘与网络行为兼容的复杂网络控制架构,并将该框架应用于脑结构网络和其他复杂网络。结果,我们发现脑网络具有由少量控制节点支配的分布式且重叠的控制架构,这可能是大脑强大且高效功能的原因。此外,我们的人工网络进化分析表明,当脑网络朝着增强稳健性和效率的方向进化时,其分布式且重叠的控制架构就会出现。