Cognitive Neurophysiology Laboratory, School of Psychology, University of Exeter, UK.
Clin Neurophysiol. 2011 Feb;122(2):267-77. doi: 10.1016/j.clinph.2010.06.033. Epub 2010 Jul 31.
The present study examined the benefit of rapid alternation of EEG and fMRI (a common strategy for avoiding artifact caused by rapid switching of MRI gradients) for detecting experimental modulations of ERPs in combined EEG-fMRI. The study also assessed the advantages of aiding the extraction of specific ERP components by means of signal decomposition using Independent Component Analysis (ICA).
'Go-nogo' task stimuli were presented either during fMRI scanning or in the gaps between fMRI scans, resulting in 'gradient' and 'no-gradient' ERPs. 'Go-nogo' differences in the N2 and P3 components were subjected to conventional ERP analysis, as well as single-trial and reliability analyses.
Comparable N2 and P3 enhancement on 'nogo' trials was found in the 'gradient' and 'no-gradient' ERPs. ICA-based signal decomposition resulted in better validity (as indicated by topography), greater stability and lower measurement error of the predicted ERP effects.
While there was little or no benefit of acquiring ERPs in the gaps between fMRI scans, ICA decomposition did improve the detection of experimental ERP modulations.
Simultaneous and continuous EEG-fMRI acquisition is preferable to interleaved protocols. ICA-based decomposition is useful not only for artifact cancellation, but also for the extraction of specific ERP components.
本研究考察了在 EEG-fMRI 中检测实验性 ERP 调制时，快速交替 EEG 和 fMRI（一种避免 MRI 梯度快速切换引起的伪影的常用策略）的益处。该研究还评估了通过独立成分分析（ICA）进行信号分解来辅助提取特定 ERP 成分的优势。
“Go-nogo”任务刺激在 fMRI 扫描期间或在 fMRI 扫描之间呈现，导致出现“梯度”和“无梯度”ERP。对 N2 和 P3 成分的“Go-nogo”差异进行了传统的 ERP 分析，以及单次和可靠性分析。
在“梯度”和“无梯度”ERP 中发现“nogo”试验上 N2 和 P3 增强相当。基于 ICA 的信号分解导致预测 ERP 效应的有效性更高（如地形所示）、稳定性更好且测量误差更低。
虽然在 fMRI 扫描之间获取 ERP 几乎没有或没有好处，但 ICA 分解确实提高了实验性 ERP 调制的检测能力。
同步和连续的 EEG-fMRI 采集优于交错协议。基于 ICA 的分解不仅有助于消除伪影，还有助于提取特定的 ERP 成分。
