Ding Mingzhou, Mo Jue, Schroeder Charles E, Wen Xiaotong
Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Fl 32611, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5916-8. doi: 10.1109/IEMBS.2011.6091463.
Multielectrode neurophysiological recording and functional brain imaging produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. Neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has identified Granger causality as a promising technique to furnish this capability. In this paper, we first introduce the concept of Granger causality and then present results from the application of this technique to multichannel local field potential data from an awake-behaving monkey.
多电极神经生理学记录和功能性脑成像会产生大量数据。多元时间序列分析为分析这些数据中的神经交互模式提供了基本框架。神经交互是有方向性的。因此，能够评估神经元交互的方向性是理解神经计算协作本质的一项非常需要的能力。过去几年的研究已将格兰杰因果关系确定为提供此能力的一种很有前景的技术。在本文中，我们首先介绍格兰杰因果关系的概念，然后展示将该技术应用于一只清醒行为猴子的多通道局部场电位数据所得到的结果。
