School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China.
J Neural Eng. 2020 Apr 17;17(2):026024. doi: 10.1088/1741-2552/ab79f5.
Recent works have shown that flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorders of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels.
By applying multilayer network methods, we calculated time-resolved functional connectivity from source-level EEG data in different frequency bands. At various time scales, we explored how the human brain changes its community structure and traverses across defined network states (integrated and segregated states) in subjects with different consciousness levels.
Network switching in the human brain is decreased with increasing time scale opposite to that in random systems. Transitions of community assignment (denoted by flexibility) are negatively correlated with the consciousness levels (particularly in the alpha band) at short time scales. At long time scales, the opposite trend is found. Compared to healthy controls, patients show a new balance between dynamic segregation and integration, with decreased proportion and mean duration of segregated state (contrary to those of integrated state) at small scales.
These findings may contribute to the development of EEG-based network analysis and shed new light on the pathological mechanisms of neurological disorders like DOC.
最近的研究表明，灵活的信息处理与大脑功能相关的网络重新配置密切相关。然而，网络切换对于意识的作用还没有得到充分的探索，这种转变是否可以指示意识障碍（DOC）患者的行为表现尚不清楚。在这里，我们研究了脑网络（状态）随时间的切换与意识水平之间的关系。
通过应用多层网络方法，我们从不同频带的源级 EEG 数据中计算了时变功能连接。在不同的时间尺度上，我们探索了人类大脑如何改变其社区结构，并在不同意识水平的受试者中穿越定义的网络状态（集成和隔离状态）。
与随机系统相反，随着时间尺度的增加，人脑的网络切换减少。社区分配的转变（用灵活性表示）与意识水平呈负相关（特别是在 alpha 波段）在短时间尺度上。在长时间尺度上，则发现相反的趋势。与健康对照组相比，患者在小尺度上表现出动态隔离和整合之间的新平衡，隔离状态的比例和平均持续时间减少（与整合状态相反）。
这些发现可能有助于基于 EEG 的网络分析的发展，并为 DOC 等神经障碍的病理机制提供新的见解。
