Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model
Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model
复制标题
DOI：
10.1016/j.neuroimage.2004.02.013
复制
发表时间：
2004-06-01
期刊：
影响因子：
5.7
通讯作者：
Friston, KJ
中科院分区：
文献类型：
--
作者：
Kiebel, SJ;Friston, KJ
在统计参数映射（SPM）的背景下，我们描述了一个事件相关电位（ERP）的时间模型。简而言之，我们使用一些适当的反解将通道数据投射到二维头皮表面或三维大脑空间。然后，我们以质量单变量的方式处理时空数据。这隐含地将模型分解为空间和时间组件。本文的关键贡献在于使用了观测模型，该模型明确区分了erp表达中的观测误差和变化。这种区别是通过采用两级层次模型来实现的，其中第一级模型模拟了ERP在受试者和试验类型内的效应，而第二级模型则模拟了ERP在试验类型和受试者之间的表达差异。通过将ERP数据的分析带入经典的分层（即混合效应）框架，许多明显不同的方法（例如，传统的P300分析和刺激锁定振荡的时频分析）可以在相同的估计和推理过程中协调一致。推断以正常的方式进行，使用t或F统计量来测试t或局部于刺激时间或某些时频窗的效应。F统计的使用是经典方法的重要推广，因为它允许人们测试多维子空间（即未知但受约束的形式）中的效果。我们描述了分析程序、基本理论，并将其性能与现有技术进行了比较。(C) 2004爱思唯尔公司版权所有。
In this paper, we describe a temporal model for event-related potentials (ERP) in the context of statistical parametric mapping (SPM). In brief, we project channel data onto a two-dimensional scalp surface or into three-dimensional brain space using some appropriate inverse solution. We then treat the spatiotemporal data in a mass-univariate fashion. This implicitly factorises the model into spatial and temporal components. The key contribution of this paper is the use of observation models that afford an explicit distinction between observation error and variation in the expression of ERPs. This distinction is created by employing a two-level hierarchical model, in which the first level models the ERP effects within-subject and trial type, while the second models differences in ERP expression among trial types and subjects. By bringing the analysis of ERP data into a classical hierarchical (i.e., mixed effects) framework, many apparently disparate approaches (e.g., conventional P300 analyses and time-frequency analyses of stimulus-locked oscillations) can be reconciled within the same estimation and inference procedure. Inference proceeds in the normal way using t or F statistics to test t. or effects that are localised in peristimulus time or in some time-frequency window. The use of F statistics is an important generalisation of classical approaches, because it allows one to test for effects that lie in a multidimensional subspace (i.e., of unknown but constrained form). We describe the analysis procedures, the underlying theory and compare its performance to established techniques. (C) 2004 Elsevier Inc. All rights reserved.