Institute of Information Science and Engineering, Yanshan University, Qinhuangdao, China.
Neural Netw. 2010 Aug;23(6):698-704. doi: 10.1016/j.neunet.2010.04.003. Epub 2010 Apr 29.
Synchronization is an important mechanism that helps in understanding information processing in a normal or abnormal brain. In this paper, we propose a new method to estimate the genuine and random synchronization indexes in multivariate neural series, denoted as GSI (genuine synchronization index) and RSI (random synchronization index), by means of a correlation matrix analysis and surrogate technique. The performance of the method is evaluated by using a multi-channel neural mass model (MNMM), including the effects of different coupling coefficients, signal to noise ratios (SNRs) and time-window widths on the estimation of the GSI and RSI. Results show that the GSI and the RSI are superior in description of the synchronization in multivariate neural series compared to the S-estimator. Furthermore, the proposed method is applied to analyze a 21-channel scalp electroencephalographic recording of a 35 year-old male who suffers from mesial temporal lobe epilepsy. The GSI and the RSI at different frequency bands during the epileptic seizure are estimated. The present results could be helpful for us to understand the synchronization mechanism of epileptic seizures.
同步是一种重要的机制，有助于理解正常或异常大脑中的信息处理。在本文中，我们提出了一种新的方法，通过相关矩阵分析和替代技术来估计多变量神经序列中的真实和随机同步指数，分别表示为 GSI（真实同步指数）和 RSI（随机同步指数）。该方法的性能通过使用多通道神经质量模型（MNMM）进行评估，包括不同耦合系数、信噪比（SNR）和时间窗宽度对 GSI 和 RSI 估计的影响。结果表明，与 S-估计器相比，GSI 和 RSI 更能描述多变量神经序列中的同步。此外，该方法还应用于分析一名 35 岁男性的 21 通道头皮脑电图记录，该男性患有内侧颞叶癫痫。在癫痫发作期间估计了不同频带的 GSI 和 RSI。本研究结果有助于我们理解癫痫发作的同步机制。
