Goethe University, Brain Imaging Center, MEG Unit, Heinrich Hoffmann Strasse 10, 60528 Frankfurt, Germany.
Prog Biophys Mol Biol. 2011 Mar;105(1-2):80-97. doi: 10.1016/j.pbiomolbio.2010.11.006. Epub 2010 Nov 27.
The analysis of cortical and subcortical networks requires the identification of their nodes, and of the topology and dynamics of their interactions. Exploratory tools for the identification of nodes are available, e.g. magnetoencephalography (MEG) in combination with beamformer source analysis. Competing network topologies and interaction models can be investigated using dynamic causal modelling. However, we lack a method for the exploratory investigation of network topologies to choose from the very large number of possible network graphs. Ideally, this method should not require a pre-specified model of the interaction. Transfer entropy--an information theoretic implementation of Wiener-type causality--is a method for the investigation of causal interactions (or information flow) that is independent of a pre-specified interaction model. We analysed MEG data from an auditory short-term memory experiment to assess whether the reconfiguration of networks implied in this task can be detected using transfer entropy. Transfer entropy analysis of MEG source-level signals detected changes in the network between the different task types. These changes prominently involved the left temporal pole and cerebellum--structures that have previously been implied in auditory short-term or working memory. Thus, the analysis of information flow with transfer entropy at the source-level may be used to derive hypotheses for further model-based testing.
皮质和皮质下网络的分析需要识别它们的节点，以及它们的拓扑结构和相互作用的动力学。节点识别的探索性工具是可用的，例如结合束形成源分析的脑磁图（MEG）。使用动态因果建模可以研究竞争的网络拓扑结构和相互作用模型。然而，我们缺乏一种方法来探索性地研究网络拓扑结构，以从大量可能的网络图中进行选择。理想情况下，这种方法不应该需要预先指定的相互作用模型。转移熵是一种信息论实现的 Wiener 型因果关系，是一种用于研究因果相互作用（或信息流）的方法，它不依赖于预先指定的相互作用模型。我们分析了听觉短期记忆实验的 MEG 数据，以评估使用转移熵是否可以检测到这个任务中隐含的网络重新配置。MEG 源水平信号的转移熵分析检测到不同任务类型之间网络的变化。这些变化主要涉及左侧颞极和小脑——这些结构以前被暗示与听觉短期或工作记忆有关。因此，在源水平上使用转移熵分析信息流可能被用于得出基于模型的进一步测试的假设。
