Exploration of interictal to ictal transition in epileptic seizures using a neural mass model.

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2024-06-01 Epub Date: 2023-05-16 DOI:10.1007/s11571-023-09976-6
Chunfeng Yang, Qingbo Luo, Huazhong Shu, Régine Le Bouquin Jeannès, Jianqing Li, Wentao Xiang
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引用次数: 0

Abstract

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-023-09976-6.

Abstract Image

应用神经质量模型探讨癫痫发作发作间期到发作期的转换
癫痫发作通常可分为三个阶段:发作间期、发作前和发作期。然而,大脑中从发作间期向发作期活动过渡的癫痫发作涉及神经元群抑制和兴奋之间复杂的相互作用。为了在单个群体的水平上探索这一机制,本文采用了一种名为 "基于生理学的完整模型(cPBM)"的神经群模型来重建脑电图(EEG)信号,并根据记录的十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。由于癫痫信号具有频谱特征,因此采用了频谱动态因果建模(DCM),通过最大化功率谱密度(PSD)框架下的自由能和估计 cPBM 参数来量化这些频率特性。此外,为了解决 DCM 可能存在的局部最大值问题,还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 方法通过逐步降低温度来调整目标函数中引入的温度,以获得相对较好的初始化,然后在每次最大化后逐步提高温度以寻找更好的估计值。结果表明:(i) 属于三个阶段的重构脑电信号及其 PSD 均可从 cPBM 的估计参数中再现;(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),并且在所有阶段都具有最佳性能(例如,重构脑电信号和 PSD 之间的均方根误差(RMSE)均低于 DCM);(iii) 与 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)、根据重建脑电信号计算的重建 PSD 与根据真实脑电信号获得的样本 PSD 之间的均方根误差分别为 0.33 ± 0.08、0.67 ± 0.37 和 0.78 ± 0.57);(iii) 锥体细胞与兴奋性中间神经元之间、锥体细胞与快速抑制性中间神经元之间的连接增加,以及 cPBM 中快速抑制性中间神经元的自环连接减少,可以解释从发作间期到发作期活动的过渡。此外,E-I 平衡(定义为锥体细胞与快速抑制性中间神经元之间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间的比率)在癫痫发作过渡期间也显著增加:在线版本包含补充材料,可查阅 10.1007/s11571-023-09976-6。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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