Boykin Erin R, Khargonekar Pramod P, Carney Paul R, Ogle William O, Talathi Sachin S
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
J Comput Neurosci. 2012 Jun;32(3):521-38. doi: 10.1007/s10827-011-0367-3. Epub 2011 Oct 14.
The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.
诸如格兰杰因果关系、部分定向相干性和相位动力学建模等数据驱动的时间序列分析技术在估计脑网络中的有效连通性方面的应用，最近在神经科学界受到了显著关注。虽然这些技术在确定脑网络不同区域之间的因果相互作用方面很有用，但仍缺乏对这些方法在识别脑网络有效连通性模式方面的比较准确性和稳健性的全面分析。在本文中，我们在耦合脉冲神经元的简单网络背景下系统地解决了这个问题。具体来说，我们开发了一种方法来评估各种有效连通性度量准确确定给定神经元网络真实有效连通性的能力。我们的方法基于决策树分类器，这些分类器使用几个仅可从实验记录数据中观察到的时间序列特征进行训练。我们表明，在这项工作中构建的分类器提供了一个通用框架，用于确定当应用于数据集时，特定的有效连通性度量是否可能产生错误结果。
