Guo Shuixia, Wu Jianhua, Ding Mingzhou, Feng Jianfeng
Department of Mathematics, Hunan Normal University, Changsha, China.
PLoS Comput Biol. 2008 May 30;4(5):e1000087. doi: 10.1371/journal.pcbi.1000087.
Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey.
振荡活动在调节从亚细胞、细胞、网络到整个生物体等各个层面的生物过程中发挥着关键作用，并且通常涉及大量相互作用的元素。我们通过引入一种名为部分格兰杰因果关系的新方法来阐明这一问题，该方法能够可靠地揭示频域中具有外生输入和潜在变量的多变量数据中的相互作用模式。该方法在玩具模型上进行了广泛测试，并成功应用于实验数据集，包括：（1）HeLa细胞周期的基因微阵列数据；（2）从绵羊颞下皮质记录的体内多电极阵列（MEA）局部场电位（LFP）；以及（3）从猕猴右半球不同部位记录的体内LFP。
