癫痫患者脑功能连通性的EEG-fMRI测量

Teresa Murta, P. Figueiredo, A. Leal
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引用次数: 3

摘要

通过同时记录5例局灶性癫痫患者的脑电图相关功能磁共振成像(EEG-fMRI)数据,对癫痫发作动力学进行了研究,目的是确定与癫痫发作有关的病灶和传播网络。研究了基于不同神经生理回归滞后(LasgM)的一般线性模型(GLM)方法、基于连接模型的动态因果模型(DCM)方法和数据驱动的Granger因果关系(GC)方法。当记录到足够数量的癫痫发作事件时,DCM分析提供了有意义和重要的结果,但通常受到数据信噪比(SNR)较差的影响。LagsM结果与临床期望之间的一致性表明LagsM可以作为一种有用的补充方法。为了确定GC方法对所解决问题的有效性,进行了仿真研究,结果表明它是不合适的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EEG-fMRI measures of functional brain connectivity in epilepsy
A study of the epileptic seizure dynamics using simultaneous recording of electroencephalography correlated functional magnetic resonance imaging (EEG-fMRI) data from 5 focal epilepsy patients undergoing pre-surgical evaluation was realised with the aim of identifying the focus and propagation network seizure-involved. A method based on the General Linear Model (GLM) at different neurophysiology regressor lags (LasgM), a connectivity model-based method, Dynamic Causal Modelling (DCM), and a data-driven method, Granger Causality (GC) were investigated. DCM analysis provided meaningful and significant results when a sufficient number of seizure events was recorded, but suffered from the generally poor data signal-to-noise ratio (SNR). The concordance between the LagsM results and the clinical expectation suggests that LagsM can be useful as a complementary approach. Intending to establish the validity of a GC approach for the problem addressed, a simulation study was performed and the results showed that it is not appropriated.
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