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Resting State Networks Analysis Using Simultaneous EEG-fMRI for Epilepsy Patient

机译:癫痫患者静息状态网络分析的同时EEG-fMRI

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The resting state EEG-fMRI has opened a new avenue in not only neuro cognitive studies but it has also found practical utility in clinical applications. We studied the Resting State Networks on Epilepsy Patient to understand the neuronal substrates involved in epilepsy. Five epilepsy patients were undertaken for simultaneous EEG-fMRI study. EEG microstates was computed and was considered as explanatory variables in the GLM design for the analysis of fMRI data in an event related design, z-stats and independent component was examined for simultaneous EEG-fMRI. We hypothesized that it's possible to analyze the affected brain areas for epileptiform discharges in epileptic patients at resting state. Microstates convolved functional image and its independent components using hybrid technique including both the neuronal and hemodynamic information was demonstrated on patients structural image. From this result we conclude that using EEG microstate and Independent Component Analysis (ICA) of resting fMRI we may examine the brain areas involved in resting state brain discharge. Also it will be useful for the analysis of EEG-fMRI data in which electrical epileptic discharge are not apparent on scalp EEG at the time of data acquisition.
机译:静止状态EEG-fMRI不仅为神经认知研究开辟了一条新途径,而且在临床应用中也发现了实用性。我们研究了癫痫患者的休息状态网络,以了解参与癫痫的神经元基质。进行了5例癫痫患者的EEG-fMRI同步研究。计算脑电微状态,并将其作为事件相关设计中fMRI数据分析的GLM设计中的解释变量,同时检查z-stats和独立成分进行EEG-fMRI。我们假设可以分析静息状态下癫痫患者的受影响大脑区域的癫痫样放电。在患者的结构图像上证明了微状态使用混合技术将功能图像及其独立组件卷积在一起,包括神经元信息和血液动力学信息。从这个结果可以得出结论,使用静息功能磁共振成像的脑电微状态和独立成分分析(ICA),我们可以检查静息状态脑放电涉及的大脑区域。这对于EEG-fMRI数据的分析也很有用,在EEG-fMRI数据采集时头皮EEG上没有明显的癫痫放电。

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