Sommerlade Linda, Henschel Kathrin, Wohlmuth Johannes, Jachan Michael, Amtage Florian, Hellwig Bernhard, Lücking Carl Hermann, Timmer Jens, Schelter Björn
FDM, Freiburg Center for Data Analysis and Modeling, University of Freiburg, Eckerstr. 1, 79104 Freiburg, Germany.
J Physiol Paris. 2009 Nov;103(6):348-52. doi: 10.1016/j.jphysparis.2009.07.005. Epub 2009 Jul 24.
The inference of interaction structures in multidimensional time series is a major challenge not only in neuroscience but in many fields of research. To gather information about the connectivity in a network from measured data, several parametric as well as non-parametric approaches have been proposed and widely examined. Today a lot of interest is focused on the evolution of the network connectivity in time which might contain information about ongoing tasks in the brain or possible dynamic dysfunctions. Therefore an extension of the current approaches towards time-resolved analysis techniques is desired. We present a parametric approach for time variant analysis, test its performance for simulated data, and apply it to real-world data.
推断多维时间序列中的相互作用结构不仅在神经科学领域，而且在许多研究领域都是一项重大挑战。为了从测量数据中收集有关网络连通性的信息，已经提出并广泛研究了几种参数化和非参数化方法。如今，很多研究兴趣都集中在网络连通性随时间的演变上，这可能包含有关大脑中正在进行的任务或可能的动态功能障碍的信息。因此，需要将当前方法扩展到时间分辨分析技术。我们提出了一种用于时变分析的参数化方法，测试了其对模拟数据的性能，并将其应用于实际数据。
