Yang Chunfeng, Luo Qingbo, Shu Huazhong, Le Bouquin Jeannès Régine, Li Jianqing, Xiang Wentao
Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing, 210096 China.
Jiangsu Provincal Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096 China.
Cogn Neurodyn. 2024 Jun;18(3):1215-1225. doi: 10.1007/s11571-023-09976-6. Epub 2023 May 16.
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.
The online version contains supplementary material available at 10.1007/s11571-023-09976-6.
癫痫发作通常可分为三个阶段：发作间期、发作前期和发作期。然而，大脑中从发作间期活动向发作期活动转变的潜在发作涉及神经元群体中抑制与兴奋之间的复杂相互作用。为了在单个群体层面探究这一机制，本文采用了一种神经团块模型，即基于完整生理学的模型（cPBM），来重建脑电图（EEG）信号，并根据为10名癫痫患者记录的开放数据集推断与兴奋 - 抑制（E - I）平衡相关的兴奋性/抑制性连接的变化。由于癫痫信号具有频谱特征，因此应用频谱动态因果模型（DCM）在功率谱密度（PSD）框架内通过最大化自由能来量化这些频率特征，并估计cPBM参数。此外，为了解决DCM可能遇到的局部最大值问题，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方法通过逐渐降低目标函数中引入的温度来调整温度，以获得相对较好的初始化，然后在每次最大化后逐渐升高温度以搜索更好的估计值。结果表明：（i）从cPBM的估计参数可以重现属于三个阶段的重建EEG信号及其PSD；（ii）与DCM、传统D - DCM和反D - DCM相比，所提出的H - DCM显示出更高的自由能和更低的均方根误差（RMSE），并且在所有阶段都提供了最佳性能（例如，在发作间期、发作前期和发作期，从重建的EEG信号计算得到的重建PSD与从真实EEG信号获得的样本PSD之间的RMSE分别为0.33±0.08、0.67±0.37和0.78±0.57）；（iii）从发作间期到发作期活动的转变可以通过cPBM中锥体细胞与兴奋性中间神经元之间以及锥体细胞与快速抑制性中间神经元之间连接的增加，以及快速抑制性中间神经元自环连接的减少来解释。此外，定义为锥体细胞到快速抑制性中间神经元的兴奋性连接与快速抑制性中间神经元自环的抑制性连接之比的E - I平衡在癫痫发作转变期间也显著增加。
在线版本包含可在10.1007/s11571 - 023 - 09976 - 6获取的补充材料。
