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Yang C, Luo Q, Shu H, Le Bouquin Jeannès R, Li J, Xiang W. Exploration of interictal to ictal transition in epileptic seizures using a neural mass model. Cogn Neurodyn 2024; 18:1215-1225. [PMID: 38826671 PMCID: PMC11143138 DOI: 10.1007/s11571-023-09976-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 02/16/2023] [Accepted: 04/19/2023] [Indexed: 06/04/2024] Open
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.
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Affiliation(s)
- Chunfeng Yang
- 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
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
| | - Qingbo Luo
- 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
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
| | - Huazhong Shu
- 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
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
| | - Régine Le Bouquin Jeannès
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes, 35000 France
| | - Jianqing Li
- Jiangsu Province Engineering Research Center for Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166 China
| | - Wentao Xiang
- Jiangsu Province Engineering Research Center for Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166 China
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Xiang W, Karfoul A, Yang C, Shu H, Le Bouquin Jeannès R. Investigation of two neural mass models for DCM-based effective connectivity inference in temporal epilepsy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106840. [PMID: 35550455 DOI: 10.1016/j.cmpb.2022.106840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around β and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. METHODS To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. RESULTS The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. CONCLUSIONS Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.
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Affiliation(s)
- Wentao Xiang
- Key Laboratory of Clinical and Medical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China; Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Ahmad Karfoul
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Chunfeng Yang
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Huazhong Shu
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Régine Le Bouquin Jeannès
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France.
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A self-organized recurrent neural network for estimating the effective connectivity and its application to EEG data. Comput Biol Med 2019; 110:93-107. [DOI: 10.1016/j.compbiomed.2019.05.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 05/12/2019] [Accepted: 05/12/2019] [Indexed: 11/21/2022]
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