Applications of random field theory to electrophysiology
Applications of random field theory to electrophysiology
复制标题
DOI：
10.1016/j.neulet.2004.10.052
复制
发表时间：
2005-02-21
影响因子：
2.5
通讯作者：
Friston, KJ
中科院分区：
文献类型：
--
作者：
Kilner, JM;Kiebel, SJ;Friston, KJ
电生理学数据的分析通常产生在一个或多个维度上连续的结果，时频图、刺激周围时间直方图和互相关函数。在随后的统计地图上作出的经典推断必须控制在地图的维度上搜索时的族明智错误（FWE）。在本文中，我们借用多重比较程序，建立在神经成像，并将其应用于电生理数据。这些程序使用随机场理论（RFT）来调整作为时间和/或频率的函数的统计数据的p值。连续统计过程的RFT调整与离散统计检验中的Bonnferonni调整具有相同的作用。在这里，通过分析单通道EEG数据的时频分解，我们表明RFT调整可以用于电生理数据的分析，并说明这种方法的优势，现有的方法。(C)2004爱思唯尔爱尔兰有限公司保留所有权利。
The analysis of electrophysiological data often produces results that are continuous in one or more dimensions, e.g., time-frequency maps, peri-stimulus time histograms, and cross-correlation functions. Classical inferences made on the ensuing statistical maps must control family wise error (FWE) when searching across the map's dimensions. In this paper, we borrow multiple comparisons procedures, established in neuroimaging, and apply them to electrophysiological data. These procedures use random field theory (RFT) to adjust p-values from statistics that are functions of time and/or frequency. This RFT adjustment for continuous statistical processes plays the same role as a Bonnferonni adjustment in the context of discrete statistical tests. Here, by analysing the time-frequency decompositions of single channel EEG data we show that RFT adjustments can be used in the analysis of electrophysiological data and illustrate the advantages of this method over existing approaches. (C) 2004 Elsevier Ireland Ltd. All rights reserved.