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目的采用独立成分分析(independent component analysis,ICA)方法,对局灶性癫痫的功能磁共振成像(functional magnetic resonance imaging,fMRI)数据进行分析,评价其在fMRI癫痫灶定位研究中的应用价值。方法采用经预处理的空间ICA方法,对12例局灶性癫痫患者18组数据进行处理。利用脑电同步功能磁共振成像(sim-ultaneous electroencephalogram and functional MRI,EEG-fMRI)检出的间期痫样发放假设驱动模式设计,对检出的各独立成分的时间序列进行多元回归排序,观察癫痫数据ICA结果的空间及时间特征。并与传统广义线性模型(general-linear model,GLM)方法进行对比,评价ICA对癫痫静态fMRI检出的效能。结果与传统GLM脑电同步功能成像方法对比(10/18),ICA方法具有较好的癫痫活动检出能力[传统广义线性法:55%(10/18);ICA法72%(13/18)]。此外,ICA方法还可以发现缺省网络等静息态脑认知网络受癫痫活动发放影响的情况。结论 ICA具有检出静态fMRI数据中间期痫样放电(interictal epiletiform discharges,IEDs)信号的能力,在癫痫活动的研究方面具有良好的应用前景。
Objective To analyze the data of functional magnetic resonance imaging (fMRI) of focal epilepsy by using independent component analysis (ICA) method and evaluate its value in the localization of fMRI epileptic foci. Methods The preprocessing spatial ICA method was used to process 18 sets of data of 12 patients with focal epilepsy. The hypothesis driving model of epileptiform delivery detected by sim-ultaneous electroencephalogram and functional MRI (EEG-fMRI) was used to carry out multiple regressions and rankings on the time series of each independent component detected. Spatial and Temporal Features of ICA Results for Epilepsy Data. Compared with the traditional generalized linear model (GLM), the performance of ICA in detecting static fMRI in epilepsy was evaluated. Results Compared with the traditional GLM imaging method (10/18), the ICA method showed better ability of detecting epilepsy (traditional generalized linear method: 55% (10/18), ICA method: 72% (13/18) )]. In addition, the ICA method can also be found in the default network and other resting cognitive states of the brain affected by the distribution of epilepsy activities. Conclusion ICA has the ability to detect the signals of static fMRI data in interictal epileptiform discharges (IEDs), and has a good prospect in the study of epileptic activity.