Taya Fumihiko, deSouza Joshua, Thakor Nitish V, Bezerianos Anastasios
IEEE Trans Neural Syst Rehabil Eng. 2017 Jun;25(6):547-556. doi: 10.1109/TNSRE.2016.2597961. Epub 2016 Sep 9.
The analysis of the topology and organization of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterizing temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multi-layer networks, in which each layer models brain connectivity at time window t + Δt. Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterized by dynamic graph metrics.
众所周知，脑网络拓扑结构和组织的分析极大地受益于图论中的网络测度。然而，为了评估脑功能连接的动态变化，需要更复杂的定量指标来表征脑拓扑特征的时间演变。将随时间变化的脑连接性简化为静态图表示很简单，但该过程会丢失在理解脑功能时可能至关重要的时间信息。为了将对功能分离和整合的理解扩展到动态层面，我们建议使用动态图指标来表征脑网络拓扑特征的时间变化。本研究通过在一个实验方案中从脑电图信号导出的动态图指标，研究了脑网络随时间的功能分离和整合：执行具有多个难度级别的复杂飞行模拟任务。我们将随时间变化的脑功能连接性建模为多层网络，其中每一层模拟时间窗口t + Δt时的脑连接性。计算动态图指标以量化网络的时间和拓扑特性。结果表明，在执行复杂任务时，脑网络呈现出动态小世界架构，具有一些频繁连接的节点或枢纽，这支持了脑内信息分离和整合随时间的平衡。结果还表明，更困难的任务导致的更大认知工作量会诱导出一个更全局高效但聚类程度更低的动态小世界功能网络。我们的研究表明，与任务相关的功能性脑网络分离和整合变化可以通过动态图指标来表征。
