Convolution models for induced electromagnetic responses.
Convolution models for induced electromagnetic responses.
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DOI：
10.1016/j.neuroimage.2012.09.014
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发表时间：
2013-01-01
期刊：
影响因子：
5.7
通讯作者：
Friston K
中科院分区：
文献类型：
--
作者：
Litvak V;Jha A;Flandin G;Friston K
在基尔纳等人中。 [Kilner, J.M.、Kiebel, S.J.、Friston, K.J., 2005。随机场理论在电生理学中的应用。神经科学。莱特。 374, 174–178.]我们使用汇总统计方法和统计参数映射描述了对电磁脑信号中诱发反应的相当一般的分析。这涉及通过测试时频图像的效果来定位诱发的反应（在刺激周围的时间和频率中），这些图像总结了每个受试者对每种试验类型的反应。传统上，这些时频摘要是使用历元数据的事后平均来估计的。然而，当诱发的反应重叠或每次试验中存在多个具有可变时间的反应成分时（例如与不同反应时间相关的刺激和反应成分），这种事后平均会失败。在这些情况下，使用 fMRI 时间序列分析中标准的卷积模型来估计响应分量是有利的。在本文中，我们描述了一种这样的方法，该方法基于对输入函数的诱导响应的普通最小二乘反卷积，该输入函数编码每个试验中不同成分的开始。这种方法有许多基本优点：例如； (i) 人们可以消除对刺激起始的诱发反应和可变时间反应的歧义； (ii) 人们可以通过参数实验因子来测试诱发反应的调制（在周围刺激时间和频率上），以及 (iii) 人们可以通过将混杂因素纳入模型来优雅地处理混杂因素，例如功率的缓慢漂移。接下来，我们根据脉冲响应基函数集考虑诱发响应的卷积模型的最佳形式，并说明使用模拟和真实 MEG 数据的反卷积估计器的实用性。 ► 我们提出了一种新方法来分析 M/EEG 中的诱发反应。 ► 一般线性模型用于对 fMRI 第一级中的连续功率进行建模。 ► 结果可以以传统的时频图像的形式呈现。 ► 我们的方法更适合可变时间和重叠事件的实验。
In Kilner et al. [Kilner, J.M., Kiebel, S.J., Friston, K.J., 2005. Applications of random field theory to electrophysiology. Neurosci. Lett. 374, 174–178.] we described a fairly general analysis of induced responses—in electromagnetic brain signals—using the summary statistic approach and statistical parametric mapping. This involves localising induced responses—in peristimulus time and frequency—by testing for effects in time–frequency images that summarise the response of each subject to each trial type. Conventionally, these time–frequency summaries are estimated using post‐hoc averaging of epoched data. However, post‐hoc averaging of this sort fails when the induced responses overlap or when there are multiple response components that have variable timing within each trial (for example stimulus and response components associated with different reaction times). In these situations, it is advantageous to estimate response components using a convolution model of the sort that is standard in the analysis of fMRI time series. In this paper, we describe one such approach, based upon ordinary least squares deconvolution of induced responses to input functions encoding the onset of different components within each trial. There are a number of fundamental advantages to this approach: for example; (i) one can disambiguate induced responses to stimulus onsets and variably timed responses; (ii) one can test for the modulation of induced responses—over peristimulus time and frequency—by parametric experimental factors and (iii) one can gracefully handle confounds—such as slow drifts in power—by including them in the model. In what follows, we consider optimal forms for convolution models of induced responses, in terms of impulse response basis function sets and illustrate the utility of deconvolution estimators using simulated and real MEG data. ► We propose a new approach to analysis of induced responses in M/EEG. ► The General Linear Model is used to model continuous power as in fMRI 1st-level. ► The results can be presented as conventional time–frequency images. ► Our method is better for experiments with variable timing and overlapping events.
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