Deptartment of Psychology, Westf. Wilhelms-University, Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience (OCC), Münster, Germany.
Cogn Neurodyn. 2010 Jun;4(2):133-49. doi: 10.1007/s11571-010-9107-z. Epub 2010 Mar 6.
A central issue in cognitive neuroscience is which cortical areas are involved in managing information processing in a cognitive task and to understand their temporal interactions. Since the transfer of information in the form of electrical activity from one cortical region will in turn evoke electrical activity in other regions, the analysis of temporal synchronization provides a tool to understand neuronal information processing between cortical regions. We adopt a method for revealing time-dependent functional connectivity. We apply statistical analyses of phases to recover the information flow and the functional connectivity between cortical regions for high temporal resolution data. We further develop an evaluation method for these techniques based on two kinds of model networks. These networks consist of coupled Rössler attractors or of coupled stochastic Ornstein-Uhlenbeck systems. The implemented time-dependent coupling includes uni- and bi-directional connectivities as well as time delayed feedback. The synchronization dynamics of these networks are analyzed using the mean phase coherence, based on averaging over phase-differences, and the general synchronization index. The latter is based on the Shannon entropy. The combination of these with a parametric time delay forms the basis of a connectivity pattern, which includes the temporal and time lagged dynamics of the synchronization between two sources. We model and discuss potential artifacts. We find that the general phase measures are remarkably stable. They produce highly comparable results for stochastic and periodic systems. Moreover, the methods proves useful for identifying brief periods of phase coupling and delays. Therefore, we propose that the method is useful as a basis for generating potential functional connective models.
认知神经科学的一个核心问题是哪些皮层区域参与管理认知任务中的信息处理，并了解它们的时间相互作用。由于以电活动形式从一个皮层区域传递信息将依次在其他区域引起电活动，因此，对时间同步性的分析提供了一种理解皮层区域之间神经元信息处理的工具。我们采用了一种揭示随时间变化的功能连接的方法。我们应用相位的统计分析来恢复高时间分辨率数据中皮层区域之间的信息流和功能连接。我们进一步基于两种模型网络为这些技术开发了一种评估方法。这些网络由耦合的 Rössler 吸引子或耦合的随机 Ornstein-Uhlenbeck 系统组成。实现的随时间变化的耦合包括单向和双向连接以及时滞反馈。这些网络的同步动力学基于平均相位相干性进行分析，该分析基于相位差的平均，以及一般同步指数。后者基于香农熵。将这些与参数时滞相结合，形成了连接模式的基础，该模式包括两个源之间同步的时间和时滞动力学。我们对潜在的伪影进行了建模和讨论。我们发现一般相位测量非常稳定。它们对随机和周期性系统产生高度可比的结果。此外，该方法对于识别短暂的相位耦合和延迟非常有用。因此，我们提出该方法可作为生成潜在功能连接模型的基础。
