无意识重构模块化脑网络动力学
Unconsciousness reconfigures modular brain network dynamics
AI总结：
本文基于深度睡眠和丙泊酚麻醉下的fMRI动态脑网络，采用多层模块度最大化分析，发现无意识会重构网络灵活性并缩小动态核心对应的最大时空模块。
摘要：
动态核心假说认为，意识与瞬时同步脑区中同时具有整合性和分化性的集群相关。我们使用动态脑网络来表示随时间变化的功能交互，并通过这些网络的灵活性和最大多层模块来评估动态核心的完整性。作为第一步，我们利用一个新开发的、用于异质时间网络中模块检测的基准来约束参数选择。接下来，我们将多层模块度最大化算法应用于由功能磁共振成像（fMRI）数据计算得到的动态脑网络；这些数据采集自深度睡眠期间和丙泊酚麻醉状态下。我们发现，无意识重构了网络灵活性，并减小了最大时空模块的规模，而我们将该最大时空模块识别为动态核心。我们的结果首次刻画了在以fMRI测量的无意识状态期间模块化脑网络动力学的特征，为人类意识的动态核心假说提供了进一步支持。
英文摘要：
The dynamic core hypothesis posits that consciousness is correlated with simultaneously integrated and differentiated assemblies of transiently synchronized brain regions. We represented time-dependent functional interactions using dynamic brain networks, and assessed the integrityof the dynamic core by means of the flexibility and largest multilayer module of these networks. As a first step, we constrained parameter selection using a newly developed benchmark for module detection in heterogeneous temporal networks. Next, we applied a multilayer modularity maximization algorithm to dynamic brain networks computed from functional magnetic resonance imaging (fMRI) data acquired during deep sleep and under propofol anesthesia. We found that unconsciousness reconfigured network flexibility and reduced the size of the largest spatiotemporal module, which we identified with the dynamic core. Our results present a first characterization of modular brain network dynamics during states of unconsciousness measured with fMRI, adding support to the dynamic core hypothesis of human consciousness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
