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Přibylová L, Ševčík J, Eclerová V, Klimeš P, Brázdil M, Meijer HGE. Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase shifts. Netw Neurosci 2024; 8:293-318. [PMID: 38562290 PMCID: PMC10954350 DOI: 10.1162/netn_a_00351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/21/2023] [Indexed: 04/04/2024] Open
Abstract
Recently, in the past decade, high-frequency oscillations (HFOs), very high-frequency oscillations (VHFOs), and ultra-fast oscillations (UFOs) were reported in epileptic patients with drug-resistant epilepsy. However, to this day, the physiological origin of these events has yet to be understood. Our study establishes a mathematical framework based on bifurcation theory for investigating the occurrence of VHFOs and UFOs in depth EEG signals of patients with focal epilepsy, focusing on the potential role of reduced connection strength between neurons in an epileptic focus. We demonstrate that synchronization of a weakly coupled network can generate very and ultra high-frequency signals detectable by nearby microelectrodes. In particular, we show that a bistability region enables the persistence of phase-shift synchronized clusters of neurons. This phenomenon is observed for different hippocampal neuron models, including Morris-Lecar, Destexhe-Paré, and an interneuron model. The mechanism seems to be robust for small coupling, and it also persists with random noise affecting the external current. Our findings suggest that weakened neuronal connections could contribute to the production of oscillations with frequencies above 1000 Hz, which could advance our understanding of epilepsy pathology and potentially improve treatment strategies. However, further exploration of various coupling types and complex network models is needed.
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Affiliation(s)
- Lenka Přibylová
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jan Ševčík
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Veronika Eclerová
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Petr Klimeš
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Milan Brázdil
- Brno Epilepsy Center, Dept. of Neurology, St. Anne’s Univ. Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic, member of the ERN EpiCARE
- Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Hil G. E. Meijer
- Department of Applied Mathematics, Techmed Centre, University of Twente, Enschede, The Netherlands
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Guo F, Cui Y, Li A, Liu M, Jian Z, Chen K, Yao D, Guo D, Xia Y. Differential patterns of very high-frequency oscillations in two seizure types of the pilocarpine-induced TLE model. Brain Res Bull 2023; 204:110805. [PMID: 37925081 DOI: 10.1016/j.brainresbull.2023.110805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/08/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
AIMS Very high-frequency oscillations (VHFOs, >500 Hz) are considered a highly sensitive biomarker of seizures. We hypothesized that VHFOs may exhibit specificity towards hypersynchronous (HYP) seizures and low-voltage fast (LVF) seizures in temporal lobe epilepsy (TLE). METHODS Local field potentials were recorded from the hippocampal network in TLE mice induced by pilocarpine. Subsequently, we analyzed the VHFO features, including their temporal-frequency characteristics and VHFO/theta coupling, during three states: baseline, preictal, and postictal for both HYP- and LVF-seizure groups. RESULTS Significant changes in most of the VHFO features were observed during the preictal state in both seizure groups. In the postictal state, VHFO features in the HYP-seizure group exhibited inverse alterations and appeared to align with those observed during baseline conditions. However, such phenomena were not observed after TLE seizures in the LVF-seizure group. CONCLUSION Our findings highlight distinct patterns of VHFO feature changes across different states of HYP seizures and LVF seizures. These results suggest that VHFOs could serve as indicative biomarkers for seizure alterations specifically associated with HYP-seizure states.
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Affiliation(s)
- Fengru Guo
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yan Cui
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Airui Li
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mingqi Liu
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhaoxin Jian
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ke Chen
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dezhong Yao
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Daqing Guo
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yang Xia
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
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Zhao X, Zhao Q, Tanaka T, Solé-Casals J, Zhou G, Mitsuhashi T, Sugano H, Yoshida N, Cao J. Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation. Cogn Neurodyn 2023; 17:703-713. [PMID: 37265654 PMCID: PMC10229525 DOI: 10.1007/s11571-022-09857-4] [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: 12/20/2021] [Revised: 06/28/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023] Open
Abstract
Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients.
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Affiliation(s)
- Xuyang Zhao
- Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Qibin Zhao
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Toshihisa Tanaka
- Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, Department of Engineering, University of Vic - Central University of Catalonia, Barcelona, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Guoxu Zhou
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | | | | | | | - Jianting Cao
- Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Japan
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Lippmann K, Klaft ZJ, Salar S, Hollnagel JO, Valero M, Maslarova A. Status epilepticus induces chronic silencing of burster and dominance of regular firing neurons during sharp wave-ripples in the mouse subiculum. Neurobiol Dis 2022; 175:105929. [DOI: 10.1016/j.nbd.2022.105929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/08/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022] Open
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Chen BW, Yang SH, Kuo CH, Chen JW, Lo YC, Kuo YT, Lin YC, Chang HC, Lin SH, Yu X, Qu B, Ro SCV, Lai HY, Chen YY. Neuro-Inspired Reinforcement Learning To Improve Trajectory Prediction In Reward-Guided Behavior. Int J Neural Syst 2022; 32:2250038. [DOI: 10.1142/s0129065722500381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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