Evidence map›Paper›PMID 38826671›Full record

ArticleCognitive neurodynamics2024

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

Chunfeng Yang, Qingbo Luo, Huazhong Shu, Régine Le Bouquin Jeannès, Jianqing Li, Wentao Xiang

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Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chunfeng YangKey Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing, 210096 China.
Qingbo LuoKey Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing, 210096 China.
Huazhong ShuKey Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing, 210096 China.
Régine Le Bouquin JeannèsCentre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China.
Jianqing LiJiangsu Province Engineering Research Center for Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166 China.
Wentao XiangJiangsu Province Engineering Research Center for Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166 China.ORCID 0000-0002-6891-6088

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Dynamic causal modellingE–I balanceEpileptic seizure transitionNeural mass modelParameters estimation

Identifiers

PMID38826671
PMCPMC11143138

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.