癫痫发作通常可以分为三个阶段:发作间,预先发作,还有ictal.然而,癫痫发作是大脑从发作间期到发作期活动过渡的基础,涉及神经元群中抑制和兴奋之间的复杂相互作用。在单一群体层面探讨这一机制,本文采用了神经质量模型,命名为完整的基于生理学的模型(cPBM),重建脑电图(EEG)信号,并根据10名癫痫患者的开放数据集推断与兴奋-抑制(E-I)平衡相关的兴奋/抑制连接的变化。由于癫痫信号显示光谱特征,通过在功率谱密度(PSD)框架中最大化自由能并估计cPBM参数,应用谱动态因果模型(DCM)来量化这些频率特性。此外,为了解决DCM可能遭受的局部最大值问题,提出了一种混合确定性DCM(H-DCM)方法,在两个方向上应用基于确定性退火的方案。H-DCM方法通过逐渐降低温度以获得相对良好的初始化,然后逐渐增加温度以在每次最大化后搜索更好的估计来调整目标函数中引入的温度。结果表明,(i)可以从cPBM的估计参数再现属于三个阶段的重建EEG信号及其PSD;(ii)与DCM相比,传统的D-DCM和反D-DCM,提出的H-DCM显示出更高的自由能和更低的均方根误差(RMSE),并且它为所有阶段提供了最佳性能(例如,从重建的EEG信号计算的重建PSD和从真实EEG信号获得的样本PSD之间的RMSE在中间为0.33±0.08、0.67±0.37和0.78±0.57,发作前和发作阶段,分别);(iii)从间期到发作期活动的过渡可以通过锥体细胞和兴奋性中间神经元之间以及锥体细胞和快速抑制性中间神经元之间的连接增加来解释,以及cPBM中快速抑制性中间神经元的自环连接减少。此外,E-I平衡,定义为从锥体细胞到快速抑制性中间神经元的兴奋性连接与与快速抑制性中间神经元的自我循环的抑制性连接之间的比率,在癫痫发作过渡期间也显着增加。
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An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation-inhibition (E-I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E-I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11571-023-09976-6.