Hemmelmann Dirk, Leistritz Lutz, Witte Herbert, Galicki Miroslaw
Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University Jena, Bachstr. 18, D-07740 Jena, Germany.
J Physiol Paris. 2009 Nov;103(6):353-60. doi: 10.1016/j.jphysparis.2009.05.008. Epub 2009 Jun 2.
This study proposes a technique for determining effective connectivity among brain regions which operates at the level of neuronal dynamics. We propose an alternative time-variant dynamic causal model (TV-DCM) where neuronal dynamic activity evolves based on generalized dynamic neural networks (GDNNs). The identification of brain architecture connectivity is carried out based on a least squares criterion and on a global search technique. Computer simulations carried out in the paper show that TV-DCM may provide multiple solutions, i.e. a set of different architectures all of which approximate the data equally well. Numerical comparisons between TV-DCM and DCM are also given. In order to determine the unique causal structure of brain regions, we apply an additional criterion, i.e. an estimation of generalization error, known from the theory of neural networks. Computer simulations also confirm the validity of our techniques.
本研究提出了一种用于确定大脑区域间有效连接性的技术，该技术在神经元动力学层面运行。我们提出了一种替代的时变动态因果模型（TV-DCM），其中神经元动态活动基于广义动态神经网络（GDNN）演化。大脑结构连接性的识别是基于最小二乘准则和全局搜索技术进行的。本文所进行的计算机模拟表明，TV-DCM可能会提供多种解决方案，即一组不同的架构，所有这些架构对数据的近似程度都一样好。文中还给出了TV-DCM和DCM之间的数值比较。为了确定大脑区域的独特因果结构，我们应用了一个额外的准则，即从神经网络理论中已知的泛化误差估计。计算机模拟也证实了我们技术的有效性。
