Liston Adam D, De Munck Jan C, Hamandi Khalid, Laufs Helmut, Ossenblok Pauly, Duncan John S, Lemieux Louis
Clinical and Experimental Epilepsy, Institute of Neurology, Chalfont St. Peter, Buckinghamshire SL9 0RJ, UK.
Neuroimage. 2006 Jul 1;31(3):1015-24. doi: 10.1016/j.neuroimage.2006.01.040. Epub 2006 Mar 20.
Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.
同时采集脑电图（EEG）和功能磁共振成像（fMRI）数据，能够研究癫痫患者静息状态下发作间期癫痫样放电（IEDs）的血流动力学相关性。本文探讨了两个问题：（1）在fMRI分析的统计建模中，IED分类的半自动化；（2）改进IED检测以提高实验性fMRI效率。对于具有多个IED发生器的患者，当fMRI分析模型区分不同形态和场的IED时，对与IED相关的血氧水平依赖（BOLD）信号变化的敏感性可以提高。为了减少视觉上IED分类的主观性，我们基于EEG事件的时空聚类实现了一个半自动化系统。我们使用来自一名局灶性癫痫患者的EEG-fMRI数据说明了该技术的实用性，在该患者中，通过视觉识别出202次IED，然后将其半自动聚类为四个簇。为了进行fMRI分析，对每个IED簇分别进行建模。这揭示了与三种不同IED类别相对应的不同区域中与IED相关的BOLD激活。在第二步中，使用信号空间投影（SSP）将头皮EEG投影到与每个IED簇对应的偶极子上。这导致了123次先前未识别的IED，在一般线性模型（GLM）中纳入这些IED，如显著的BOLD激活所示，提高了实验效率。我们还表明，面对视觉检测到的IED集合中的波动，额外IED的检测是稳健的。我们得出结论，自动化的IED分类可以导致更客观的IED的fMRI模型，并显著提高敏感性。
