Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
Neuroimage. 2012 Jul 16;61(4):1383-93. doi: 10.1016/j.neuroimage.2012.03.028. Epub 2012 Mar 15.
To improve the sensitivity and specificity of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) it is prudent to devise modelling strategies explaining the residual variance. The purpose of this study is to investigate the potential value of including additional regressors for physiological activities, derived from video-EEG, in the modelling of haemodynamic patterns linked to interictal epileptiform discharges (IEDs) using simultaneously recorded video-EEG-fMRI.
Ten patients with IED (focal epilepsy: 6, idiopathic generalized epilepsy (IGE):4) were studied. BOLD-sensitive fMRI images were acquired on a 3T MRI scanner. 64-channel EEG was recorded using MR-compatible system. A custom made, dual-video-camera system synchronised with EEG was used to record video simultaneously. IEDs and physiological activities were identified and labelled on video-EEG using Brain Analyzer2. fMRI time-series data were pre-processed and analysed using SPM5 software. Two general linear models (GLM) were created; GLM1: IEDs were convolved with the canonical haemodynamic response function and its derivatives. Realignment parameters and pulse regressors were included in the design matrix as confounds, GLM2: GLM1 and additional regressors identified on video-EEG including: eye blinks, hand or foot movement, chewing and swallowing were also included in the design matrix. SPM [F] maps (p<0.05, corrected for family wise error and p<0.001, uncorrected) were generated for both models. We compared the resulting blood oxygen level dependent (BOLD) maps for cluster size, statistical significance and degree of concordance with the irritative zone.
BOLD changes relating to physiological activities were generally seen in expected brain areas. In patients with focal epilepsy, the extent and Z-score of the IED-related global maximum BOLD clusters increased in 4/6 patients and additional IED-related BOLD clusters were observed in 3/6 patients for GLM2. Also, the degree of concordance of IED-related maps with irritative zone improved for one patient for GLM2 and was unchanged for the other cases. In patients with IGE, the size and statistical significance for global maximum and other BOLD clusters increased in 2/4 patients. We conclude that the inclusion of additional regressors, derived from video based information, in the design matrix explains a greater amount of variance and can reveal additional IED-related BOLD clusters which may be part of the epileptic networks.
为了提高脑电功能磁共振成像(EEG-fMRI)的灵敏度和特异性,谨慎地设计解释残余方差的建模策略很有必要。本研究的目的是通过同时记录视频-EEG-fMRI,来探讨在建模与癫痫样放电(IED)相关的血液动力学模式时,纳入源自视频-EEG 的生理活动的额外回归量的潜在价值。
研究了 10 例 IED 患者(局灶性癫痫:6 例,特发性全面性癫痫(IGE):4 例)。在 3T MRI 扫描仪上采集血氧水平依赖(BOLD)敏感 fMRI 图像。使用与磁共振兼容的系统记录 64 通道 EEG。使用同步的定制双通道摄像机系统同步记录视频。使用 Brain Analyzer2 在视频-EEG 上识别和标记 IED 和生理活动。使用 SPM5 软件对 fMRI 时间序列数据进行预处理和分析。创建了两个通用线性模型(GLM);GLM1:将 IED 与典型的血液动力学响应函数及其导数卷积。将重对齐参数和脉冲回归量作为混杂因素包含在设计矩阵中,GLM2:GLM1 和在视频-EEG 上识别的额外回归量,包括:眨眼、手或脚运动、咀嚼和吞咽也包含在设计矩阵中。对于两个模型,生成 SPM [F]映射(p<0.05,校正家族性错误和 p<0.001,未校正)。我们比较了模型的簇大小、统计显著性和与刺激性区域的一致性程度的 BOLD 图。
与生理活动相关的 BOLD 变化通常出现在预期的大脑区域。在局灶性癫痫患者中,4/6 例患者的 IED 相关全局最大 BOLD 簇的大小和 Z 分数增加,6/6 例患者观察到额外的 IED 相关 BOLD 簇,对于 GLM2。此外,对于 GLM2,一名患者的 IED 相关图与刺激性区域的一致性程度提高,而其他病例则保持不变。在 IGE 患者中,2/4 例患者的全局最大和其他 BOLD 簇的大小和统计显著性增加。我们得出的结论是,在设计矩阵中纳入源自视频的信息的额外回归量可以解释更多的方差,并可以揭示可能是癫痫网络一部分的额外的 IED 相关的 BOLD 簇。