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基于多神经群体模型的癫痫间期和发作期 EEG 的神经生理分析。

Neurophysiological Analysis of the Genesis Mechanism of EEG During the Interictal and Ictal Periods Using a Multiple Neural Masses Model.

机构信息

1 Department of Information Engineering, Binzhou University, Binzhou 256600, P. R. China.

出版信息

Int J Neural Syst. 2018 Feb;28(1):1750027. doi: 10.1142/S0129065717500277. Epub 2017 Apr 11.

DOI:10.1142/S0129065717500277
PMID:28639464
Abstract

Electroencephalography (EEG) is an important method to investigate the neurophysiological mechanism underlying epileptogenesis to identify new therapies for the treatment of epilepsy. The neurophysiologically based neural mass model (NMM) can build a bridge between signal processing and neurophysiology, which can be used as a platform to explore the neurophysiological mechanism of epileptogenesis. Most EEG signals cannot be regarded as the outputs of a single NMM with identical model parameters. The outputs of NMM are simple because the diversity of neural signals in the same NMM is ignored. To improve the simulation of EEG signals, a multiple NMM is proposed, the output of which is the linear combination of the outputs of all NMMs. The NMM number is not fixed and is minimized under the premise of guaranteeing the fitting effect. Orthogonal matching pursuit is used to solve a constrained [Formula: see text] norm minimization problem for NMM number and the strength of every NMM. The results showed that the NMM number was significantly lower during the ictal period than during the interictal period, and the strength of major NMMs increased. This indicates that neural masses fuse into fewer larger neural masses with greater strength. The distribution of excitatory and inhibitory strength during the ictal and interictal periods was similar, whereas the excitation/inhibition ratio was higher during the ictal period than during the interictal period.

摘要

脑电图(EEG)是研究癫痫发生的神经生理机制以确定治疗癫痫新疗法的重要方法。基于神经生理学的神经质量模型(NMM)可以在信号处理和神经生理学之间架起桥梁,可作为探索癫痫发生的神经生理机制的平台。大多数 EEG 信号不能被视为具有相同模型参数的单个 NMM 的输出。由于忽略了相同 NMM 中神经信号的多样性,NMM 的输出是简单的。为了改善 EEG 信号的模拟,提出了一种多 NMM,其输出是所有 NMM 输出的线性组合。NMM 的数量不是固定的,并且在保证拟合效果的前提下最小化。使用正交匹配追踪来解决 NMM 数量和每个 NMM 的强度的 [Formula: see text] 范数最小化问题。结果表明,在发作期 NMM 的数量明显低于发作间期,并且主要 NMM 的强度增加。这表明神经质量融合成具有更大强度的更少更大的神经质量。发作期和发作间期兴奋和抑制强度的分布相似,而发作期的兴奋/抑制比高于发作间期。

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