1
|
Amaro Alves Romariz S, Klippel Zanona Q, Vendramin Pasquetti M, Cardozo Muller G, de Almeida Xavier J, Hermanus Schoorlemmer G, Monteiro Longo B, Calcagnotto ME. Modification of pre-ictal cortico-hippocampal oscillations by medial ganglionic eminence precursor cells grafting in the pilocarpine model of epilepsy. Epilepsy Behav 2024; 159:110027. [PMID: 39217756 DOI: 10.1016/j.yebeh.2024.110027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2024] [Revised: 08/27/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
Cell replacement therapies using medial ganglionic eminence (MGE)-derived GABAergic precursors reduce seizures by restoring inhibition in animal models of epilepsy. However, how MGE-derived cells affect abnormal neuronal networks and consequently brain oscillations to reduce ictogenesis is still under investigation. We performed quantitative analysis of pre-ictal local field potentials (LFP) of cortical and hippocampal CA1 areas recorded in vivo in the pilocarpine rat model of epilepsy, with or without intrahippocampal MGE-precursor grafts (PILO and PILO+MGE groups, respectively). The PILO+MGE animals had a significant reduction in the number of seizures. The quantitative analysis of pre-ictal LFP showed decreased power of cortical and hippocampal delta, theta and beta oscillations from the 5 min. interictal baseline to the 20 s. pre-ictal period in both groups. However, PILO+MGE animals had higher power of slow and fast oscillations in the cortex and lower power of slow and fast oscillations in the hippocampus compared to the PILO group. Additionally, PILO+MGE animals exhibited decreased cortico-hippocampal synchrony for theta and gamma oscillations at seizure onset and lower hippocampal CA1 synchrony between delta and theta with slow gamma oscillations compared to PILO animals. These findings suggest that MGE-derived cell integration into the abnormally rewired network may help control ictogenesis.
Collapse
Affiliation(s)
- Simone Amaro Alves Romariz
- Laboratório de Neurofisiologia, Departamento de Fisiologia, Universidade Federal de São Paulo (UNIFESP/SP), São Paulo, Brazil
| | - Querusche Klippel Zanona
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory, Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Graduate Program in Neuroscience, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Mayara Vendramin Pasquetti
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory, Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Graduate Program in Biological Science: Biochemistry, Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Gabriel Cardozo Muller
- Graduate Program in Epidemiology, Medical School, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Graduate Program in Medical Science, Universidade do Vale do Taquari, Lajeado, RS, Brazil
| | - Jaqueline de Almeida Xavier
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory, Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Guus Hermanus Schoorlemmer
- Laboratório de Fisiologia Cardiovascular e Respiratória, Departamento de Fisiologia, Universidade Federal de São Paulo (UNIFESP/SP), São Paulo, Brazil
| | - Beatriz Monteiro Longo
- Laboratório de Neurofisiologia, Departamento de Fisiologia, Universidade Federal de São Paulo (UNIFESP/SP), São Paulo, Brazil
| | - Maria Elisa Calcagnotto
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory, Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Graduate Program in Neuroscience, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Graduate Program in Biological Science: Biochemistry, Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
| |
Collapse
|
2
|
Schroeder GM, Karoly PJ, Maturana M, Panagiotopoulou M, Taylor PN, Cook MJ, Wang Y. Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states. Brain Commun 2023; 5:fcad205. [PMID: 37693811 PMCID: PMC10484289 DOI: 10.1093/braincomms/fcad205] [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: 01/24/2023] [Revised: 06/07/2023] [Accepted: 07/18/2023] [Indexed: 09/12/2023] Open
Abstract
Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterized the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states. We then compared seizure network state occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. In most patients, the occurrence or duration of at least one seizure network state was associated with the time since implantation. Some patients had one or more seizure network states that were associated with phases of circadian and/or multidien spike rate cycles. A given seizure network state's occurrence and duration were usually not associated with the same timescale. Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a seizure network state's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures and similar principles likely apply to other neurological conditions.
Collapse
Affiliation(s)
- Gabrielle M Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Philippa J Karoly
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Matias Maturana
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
- Research Department, Seer Medical Pty Ltd., Melbourne, Victoria 3000, Australia
| | - Mariella Panagiotopoulou
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Mark J Cook
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| |
Collapse
|
3
|
Hagan B, Mujumdar R, Sahoo JP, Das A, Dutta A. Technical feasibility of multimodal imaging in neonatal hypoxic-ischemic encephalopathy from an ovine model to a human case series. Front Pediatr 2023; 11:1072663. [PMID: 37425273 PMCID: PMC10323750 DOI: 10.3389/fped.2023.1072663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/02/2023] [Indexed: 07/11/2023] Open
Abstract
Hypoxic-ischemic encephalopathy (HIE) secondary to perinatal asphyxia occurs when the brain does not receive enough oxygen and blood. A surrogate marker for "intact survival" is necessary for the successful management of HIE. The severity of HIE can be classified based on clinical presentation, including the presence of seizures, using a clinical classification scale called Sarnat staging; however, Sarnat staging is subjective, and the score changes over time. Furthermore, seizures are difficult to detect clinically and are associated with a poor prognosis. Therefore, a tool for continuous monitoring on the cot side is necessary, for example, an electroencephalogram (EEG) that noninvasively measures the electrical activity of the brain from the scalp. Then, multimodal brain imaging, when combined with functional near-infrared spectroscopy (fNIRS), can capture the neurovascular coupling (NVC) status. In this study, we first tested the feasibility of a low-cost EEG-fNIRS imaging system to differentiate between normal, hypoxic, and ictal states in a perinatal ovine hypoxia model. Here, the objective was to evaluate a portable cot-side device and perform autoregressive with extra input (ARX) modeling to capture the perinatal ovine brain states during a simulated HIE injury. So, ARX parameters were tested with a linear classifier using a single differential channel EEG, with varying states of tissue oxygenation detected using fNIRS, to label simulated HIE states in the ovine model. Then, we showed the technical feasibility of the low-cost EEG-fNIRS device and ARX modeling with support vector machine classification for a human HIE case series with and without sepsis. The classifier trained with the ovine hypoxia data labeled ten severe HIE human cases (with and without sepsis) as the "hypoxia" group and the four moderate HIE human cases as the "control" group. Furthermore, we showed the feasibility of experimental modal analysis (EMA) based on the ARX model to investigate the NVC dynamics using EEG-fNIRS joint-imaging data that differentiated six severe HIE human cases without sepsis from four severe HIE human cases with sepsis. In conclusion, our study showed the technical feasibility of EEG-fNIRS imaging, ARX modeling of NVC for HIE classification, and EMA that may provide a biomarker of sepsis effects on the NVC in HIE.
Collapse
Affiliation(s)
- Brian Hagan
- School of Engineering, University of Lincoln, Lincoln, United Kingdom
| | - Radhika Mujumdar
- School of Engineering, University of Lincoln, Lincoln, United Kingdom
| | - Jagdish P. Sahoo
- Department of Neonatology, IMS & SUM Hospital, Bhubaneswar, India
| | - Abhijit Das
- Department of Neurology, The Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
| | - Anirban Dutta
- School of Engineering, University of Lincoln, Lincoln, United Kingdom
| |
Collapse
|
4
|
Shi LJ, Li CC, Lin YC, Ding CT, Wang YP, Zhang JC. The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources. CNS Neurosci Ther 2023; 29:1423-1433. [PMID: 36815318 PMCID: PMC10068465 DOI: 10.1111/cns.14115] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/09/2023] [Accepted: 01/25/2023] [Indexed: 02/24/2023] Open
Abstract
OBJECTIVE To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization. METHODS Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality. RESULTS Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01). CONCLUSION This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.
Collapse
Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Can-Cheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Cheng-Tao Ding
- Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
| |
Collapse
|
5
|
Matos J, Peralta G, Heyse J, Menetre E, Seeck M, van Mierlo P. Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure. Bioengineering (Basel) 2022; 9:690. [PMID: 36421091 PMCID: PMC9687589 DOI: 10.3390/bioengineering9110690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 09/29/2023] Open
Abstract
Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient's cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models' evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.
Collapse
Affiliation(s)
- João Matos
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Guilherme Peralta
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Jolan Heyse
- Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
| | - Eric Menetre
- EEG and Epilepsy Unit, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Pieter van Mierlo
- Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
| |
Collapse
|
6
|
Aussel A, Ranta R, Aron O, Colnat-Coulbois S, Maillard L, Buhry L. Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations. J Comput Neurosci 2022; 50:519-535. [PMID: 35971033 DOI: 10.1007/s10827-022-00829-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 07/03/2022] [Accepted: 07/12/2022] [Indexed: 10/15/2022]
Abstract
The mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and synaptic connectivity, we study the role of micro- and mesoscale pathological conditions of the epileptic hippocampus in the generation and maintenance of seizure-like theta and interictal oscillations. We show, through the simulations of hippocampal activity during slow-wave sleep and wakefulness that: (i) both mossy fiber sprouting and sclerosis account for seizure-like theta activity, (ii) but they have antagonist effects (seizure-like activity occurrence increases with sprouting but decreases with sclerosis), (iii) though impaired potassium and chloride dynamics have little influence on the generation of seizure-like activity, they do play a role on the generation of interictal patterns, and (iv) seizure-like activity and fast ripples are more likely to occur during wakefulness and interictal spikes during sleep.
Collapse
Affiliation(s)
- Amélie Aussel
- Laboratoire Lorrain de Recherche en Informatique et ses applications (LORIA UMR 7503), University of Lorraine, 54506, Nancy, France. .,Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.
| | - Radu Ranta
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France
| | - Olivier Aron
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Sophie Colnat-Coulbois
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Louise Maillard
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Laure Buhry
- Laboratoire Lorrain de Recherche en Informatique et ses applications (LORIA UMR 7503), University of Lorraine, 54506, Nancy, France
| |
Collapse
|
7
|
Li X, Zhang H, Lai H, Wang J, Wang W, Yang X. High-Frequency Oscillations and Epileptogenic Network. Curr Neuropharmacol 2022; 20:1687-1703. [PMID: 34503414 PMCID: PMC9881061 DOI: 10.2174/1570159x19666210908165641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/26/2021] [Accepted: 08/31/2021] [Indexed: 11/22/2022] Open
Abstract
Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.
Collapse
Affiliation(s)
- Xiaonan Li
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | | | | | - Jiaoyang Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Wei Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China,Address correspondence to this author at the Bioland Laboratory, Guangzhou, China; Tel: 86+ 18515855127; E-mail:
| |
Collapse
|
8
|
Panagiotopoulou M, Papasavvas CA, Schroeder GM, Thomas RH, Taylor PN, Wang Y. Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions. Hum Brain Mapp 2022; 43:2460-2477. [PMID: 35119173 PMCID: PMC9057101 DOI: 10.1002/hbm.25796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/30/2021] [Accepted: 01/23/2022] [Indexed: 01/14/2023] Open
Abstract
Epilepsy is recognised as a dynamic disease, where both seizure susceptibility and seizure characteristics themselves change over time. Specifically, we recently quantified the variable electrographic spatio-temporal seizure evolutions that exist within individual patients. This variability appears to follow subject-specific circadian, or longer, timescale modulations. It is therefore important to know whether continuously recorded interictaliEEG features can capture signatures of these modulations over different timescales. In this study, we analyse continuous intracranial electroencephalographic (iEEG) recordings from video-telemetry units and find fluctuations in iEEG band power over timescales ranging from minutes up to 12 days. As expected and in agreement with previous studies, we find that all subjects show a circadian fluctuation in their iEEG band power. We additionally detect other fluctuations of similar magnitude on subject-specific timescales. Importantly, we find that a combination of these fluctuations on different timescales can explain changes in seizure evolutions in most subjects above chance level. These results suggest that subject-specific fluctuations in iEEG band power over timescales of minutes to days may serve as markers of seizure modulating processes. We hope that future study can link these detected fluctuations to their biological driver(s). There is a critical need to better understand seizure modulating processes, as this will enable the development of novel treatment strategies that could minimise the seizure spread, duration or severity and therefore the clinical impact of seizures.
Collapse
Affiliation(s)
- Mariella Panagiotopoulou
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Christoforos A. Papasavvas
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Gabrielle M. Schroeder
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Rhys H. Thomas
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
| | - Peter N. Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
| |
Collapse
|
9
|
Sanz-García A, Perez-Romero M, Ortega GJ. Spectral and network characterization of focal seizure types and phases. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106704. [PMID: 35220198 DOI: 10.1016/j.cmpb.2022.106704] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/27/2022] [Accepted: 02/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Currently, epileptic seizure characterization relies on several clinical features that allow their classification into different types. The present work aims to characterize both seizure types and phases based exclusively on electrophysiological characteristics. METHODS Based on the analysis of intracranial EEG recordings of 129 seizures from 22 patients obtained from the European Epilepsy Database, network and spectral measures were calculated in five-second temporal windows. Statistically significant differences between each window of the seizure phases (preictal, ictal, and postictal) and the interictal phase were used to identify/classify seizure types and their phases. A support vector machine (SVM) working on a multidimensional feature space of network and spectral measures was implemented for the classification of each seizure type; a traditional statistical approach was also conducted to highlight the underlying patterns to each seizure type or phase. RESULTS The percentage of correct classification of seizure types, corrected by chance, provided by the SVM exceeded 70%, considering all measures and the entire seizure (preictal + ictal + postictal). This percentage increased to more than 80% when all the measures during the ictal period for the depth electrodes or during the postictal for subdural electrodes were considered. Regarding the statistical approach, several measures presented a monotonic ascending and descending behavior with respect to seizure severity; these changes were observed during the ictal and postictal periods. Some measures were specific of each seizure type. CONCLUSIONS Our results provide a new framework to seizure characterization and reveal the possibility of an exclusively intracranial EEG-based classification. This could be used to build an automatic seizure classification system and provides new evidence of the network-related physiopathology of epilepsies. Thus, the novelty of this work is the possibility of differentiating seizure types based exclusively on the EEG recordings, providing evidence of the underlying patterns or characteristics to each seizure type and/or phase that would allow their optimal classification.
Collapse
Affiliation(s)
- Ancor Sanz-García
- Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, Diego de León 62, 9th floor, Madrid 28006, Spain.
| | - Miriam Perez-Romero
- Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, Diego de León 62, 9th floor, Madrid 28006, Spain
| | - Guillermo J Ortega
- Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, Diego de León 62, 9th floor, Madrid 28006, Spain; CONICET, National Scientific and Technical Research Council, Argentina; Universidad Nacional de Quilmes, Science and Technology Department, Argentina
| |
Collapse
|
10
|
Paulk AC, Zelmann R, Crocker B, Widge AS, Dougherty DD, Eskandar EN, Weisholtz DS, Richardson RM, Cosgrove GR, Williams ZM, Cash SS. Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters. Brain Stimul 2022; 15:491-508. [PMID: 35247646 PMCID: PMC8985164 DOI: 10.1016/j.brs.2022.02.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses. OBJECTIVE To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated. METHODS We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites). RESULTS Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation. CONCLUSION These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.
Collapse
Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Darin D Dougherty
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Daniel S Weisholtz
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - R Mark Richardson
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - Ziv M Williams
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
11
|
韩 长, 彭 福, 陈 财, 李 文, 张 昔, 王 星, 周 卫. [Research progress of epileptic seizure predictions based on electroencephalogram signals]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2021; 38:1193-1202. [PMID: 34970903 PMCID: PMC9927116 DOI: 10.7507/1001-5515.202105052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/15/2021] [Indexed: 06/14/2023]
Abstract
As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.
Collapse
Affiliation(s)
- 长明 韩
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
- 山东中科先进技术研究院有限公司(济南 250000)Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250000, P.R.China
| | - 福来 彭
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 财 陈
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 文超 李
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 昔坤 张
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 星维 王
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 卫东 周
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| |
Collapse
|
12
|
Yu T, Liu X, Wu J, Wang Q. Electrophysiological Biomarkers of Epileptogenicity in Alzheimer's Disease. Front Hum Neurosci 2021; 15:747077. [PMID: 34916917 PMCID: PMC8669481 DOI: 10.3389/fnhum.2021.747077] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Cortical network hyperexcitability is an inextricable feature of Alzheimer’s disease (AD) that also might accelerate its progression. Seizures are reported in 10–22% of patients with AD, and subclinical epileptiform abnormalities have been identified in 21–42% of patients with AD without seizures. Accurate identification of hyperexcitability and appropriate intervention to slow the compromise of cognitive functions of AD might open up a new approach to treatment. Based on the results of several studies, epileptiform discharges, especially those with specific features (including high frequency, robust morphology, right temporal location, and occurrence during awake or rapid eye movement states), frequent small sharp spikes (SSSs), temporal intermittent rhythmic delta activities (TIRDAs), and paroxysmal slow wave events (PSWEs) recorded in long-term scalp electroencephalogram (EEG) provide sufficient sensitivity and specificity in detecting cortical network hyperexcitability and epileptogenicity of AD. In addition, magnetoencephalogram (MEG), foramen ovale (FO) electrodes, and computational approaches help to find subclinical seizures that are invisible on scalp EEGs. We performed a comprehensive analysis of the aforementioned electrophysiological biomarkers of AD-related seizures.
Collapse
Affiliation(s)
- Tingting Yu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xiao Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| |
Collapse
|
13
|
Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol 2021; 12:675728. [PMID: 34054713 PMCID: PMC8155381 DOI: 10.3389/fneur.2021.675728] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
Collapse
Affiliation(s)
- William J Bosl
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Health Informatics Program, University of San Francisco, San Francisco, CA, United States
| | - Alan Leviton
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| |
Collapse
|
14
|
Lazzarotto G, Klippel Zanona Q, Cagliari Zenki K, Calcagnotto ME. Effect of Memantine on Pentylenetetrazol-induced Seizures and EEG Profile in Animal Model of Cortical Malformation. Neuroscience 2021; 457:114-124. [PMID: 33465407 DOI: 10.1016/j.neuroscience.2020.12.039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 11/27/2020] [Accepted: 12/31/2020] [Indexed: 11/28/2022]
Abstract
Developmental cortical malformations (DCM) are one of the main causes of refractory epilepsy. Many are the mechanisms underlying the hyperexcitability in DCM, including the important contribution of N-methyl-D-aspartate receptors (NMDAR). NMDAR blockers are shown to abolish seizures and epileptiform activity. Memantine, a NMDAR antagonist used to treat Alzheimeŕs disease, has been recently investigated as a possible treatment for other neurological disorders. However, the effects on preventing or diminishing seizures are controversial. Here we aimed to evaluate the effects of memantine on pentylenetetrazole (PTZ)-induced seizures in the freeze-lesion (FL) model. Bilateral cortical microgyria were induced (FL) or not (Sham) in male Wistar neonate rats. At P30, subdural electrodes were implanted and 7 days later, video-EEG was recorded in animals receiving either memantine (FL-M or Sham-M) or saline (FL-S or Sham-S), followed by PTZ. Seizures were evaluated by video-EEG during one hour and scored according to Racine scale. The video-EEG analyses revealed that the number of seizures and the total duration of stage IV-V seizures developed during the 1 h-period increased after memantine application in all groups. The EEG power spectral density (PSD) analysis showed an increased PSD of pre-ictal delta in Sham-M animals and increased PSD of slow, middle and fast gamma oscillations after memantine injection that persists during the pre-ictal period in all groups. Our findings suggested that memantine was unable to control the PTZ-induced seizures and that the associated enhancement of PSD of gamma oscillations may contribute to the increased probability of seizure development in these animals.
Collapse
Affiliation(s)
- Gabriela Lazzarotto
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Querusche Klippel Zanona
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Kamila Cagliari Zenki
- Graduate Program in Biological Sciences: Biochemistry, Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Maria Elisa Calcagnotto
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Graduate Program in Biological Sciences: Biochemistry, Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
| |
Collapse
|
15
|
Kaufmann E, Seethaler M, Lauseker M, Fan M, Vollmar C, Noachtar S, Rémi J. Who seizes longest? Impact of clinical and demographic factors. Epilepsia 2020; 61:1376-1385. [DOI: 10.1111/epi.16577] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 05/15/2020] [Accepted: 05/15/2020] [Indexed: 11/30/2022]
Affiliation(s)
- Elisabeth Kaufmann
- Department of Neurology Epilepsy Center University HospitalLudwig Maximilian University of Munich Munich Germany
| | - Magdalena Seethaler
- Department of Neurology Epilepsy Center University HospitalLudwig Maximilian University of Munich Munich Germany
| | - Michael Lauseker
- Institute for Medical Information Processing, Biometry, and Epidemiology Ludwig Maximilian University of Munich Munich Germany
| | - Min Fan
- Institute for Medical Information Processing, Biometry, and Epidemiology Ludwig Maximilian University of Munich Munich Germany
| | - Christian Vollmar
- Department of Neurology Epilepsy Center University HospitalLudwig Maximilian University of Munich Munich Germany
| | - Soheyl Noachtar
- Department of Neurology Epilepsy Center University HospitalLudwig Maximilian University of Munich Munich Germany
| | - Jan Rémi
- Department of Neurology Epilepsy Center University HospitalLudwig Maximilian University of Munich Munich Germany
| |
Collapse
|
16
|
Schroeder GM, Diehl B, Chowdhury FA, Duncan JS, de Tisi J, Trevelyan AJ, Forsyth R, Jackson A, Taylor PN, Wang Y. Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy. Proc Natl Acad Sci U S A 2020; 117:11048-11058. [PMID: 32366665 PMCID: PMC7245106 DOI: 10.1073/pnas.1922084117] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.
Collapse
Affiliation(s)
- Gabrielle M Schroeder
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - John S Duncan
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Andrew J Trevelyan
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Rob Forsyth
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Andrew Jackson
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom;
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| |
Collapse
|
17
|
DAS A, Cash SS, Sejnowski TJ. Heterogeneity of Preictal Dynamics in Human Epileptic Seizures. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:52738-52748. [PMID: 32411567 PMCID: PMC7224217 DOI: 10.1109/access.2020.2981017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.
Collapse
Affiliation(s)
- Anup DAS
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA 94305 USA
| | - Sydney S Cash
- Massachusetts General Hospital, Boston, MA 02114 USA
| | - Terrence J Sejnowski
- Division of Biological Sciences and Institute of Neural Computation, University of California, San Diego, La Jolla, CA 92093 USA
| |
Collapse
|
18
|
Burrello A, Schindler K, Benini L, Rahimi A. Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings. IEEE Trans Biomed Eng 2020; 67:601-613. [DOI: 10.1109/tbme.2019.2919137] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
19
|
Jiang H, Cai Z, Worrell GA, He B. Multiple Oscillatory Push-Pull Antagonisms Constrain Seizure Propagation. Ann Neurol 2019; 86:683-694. [PMID: 31566799 PMCID: PMC6856814 DOI: 10.1002/ana.25583] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 08/06/2019] [Accepted: 08/18/2019] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Drug-resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low-frequency activity (LFA), high-frequency activity (HFA), or low-to-high cross-frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear. METHODS We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within-frequency and cross-frequency directional connectivity were estimated by an adaptive directed transfer function and cross-frequency directionality, respectively. RESULTS In the within-frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross-frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions' HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions' HFA. INTERPRETATION Our results suggest that the secondary generalization of focal seizures is regulated by numerous within- and cross-frequency push-pull dynamics, potentially reflecting impaired excitation-inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683-694.
Collapse
Affiliation(s)
- Haiteng Jiang
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA
| | - Zhengxiang Cai
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA
| | | | - Bin He
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA
| |
Collapse
|
20
|
Hippocampal CA1 and cortical interictal oscillations in the pilocarpine model of epilepsy. Brain Res 2019; 1722:146351. [DOI: 10.1016/j.brainres.2019.146351] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/17/2019] [Accepted: 07/23/2019] [Indexed: 01/25/2023]
|
21
|
Lam AD, Cole AJ, Cash SS. New Approaches to Studying Silent Mesial Temporal Lobe Seizures in Alzheimer's Disease. Front Neurol 2019; 10:959. [PMID: 31551916 PMCID: PMC6737997 DOI: 10.3389/fneur.2019.00959] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 08/20/2019] [Indexed: 12/13/2022] Open
Abstract
Silent seizures were discovered in mouse models of Alzheimer's disease over 10 years ago, yet it remains unclear whether these seizures are a salient feature of Alzheimer's disease in humans. Seizures that arise early in the course of Alzheimer's disease most likely originate from the mesial temporal lobe, one of the first structures affected by Alzheimer's disease pathology and one of the most epileptogenic regions of the brain. Several factors greatly limit our ability to identify mesial temporal lobe seizures in patients with Alzheimer's disease, however. First, mesial temporal lobe seizures can be difficult to recognize clinically, as their accompanying symptoms are often subtle or even non-existent. Second, electrical activity arising from the mesial temporal lobe is largely invisible on the scalp electroencephalogram (EEG), the mainstay of diagnosis for epilepsy in this population. In this review, we will describe two new approaches being used to study silent mesial temporal lobe seizures in Alzheimer's disease. We will first describe the methodology and application of foramen ovale electrodes, which captured the first recordings of silent mesial temporal lobe seizures in humans with Alzheimer's disease. We will then describe machine learning approaches being developed to non-invasively identify silent mesial temporal lobe seizures on scalp EEG. Both of these tools have the potential to elucidate the role of silent seizures in humans with Alzheimer's disease, which could have important implications for early diagnosis, prognostication, and development of targeted therapies for this population.
Collapse
Affiliation(s)
- Alice D. Lam
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Andrew J. Cole
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Sydney S. Cash
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| |
Collapse
|
22
|
Pizarro D, Ilyas A, Chaitanya G, Toth E, Irannejad A, Romeo A, Riley KO, Iasemidis L, Pati S. Spectral organization of focal seizures within the thalamotemporal network. Ann Clin Transl Neurol 2019; 6:1836-1848. [PMID: 31468745 PMCID: PMC6764631 DOI: 10.1002/acn3.50880] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 01/08/2023] Open
Abstract
Objective To investigate dynamic changes in neural activity between the anterior nucleus of the thalamus (ANT) and the seizure onset zone (SOZ) in patients with drug‐resistant temporal lobe epilepsy (TLE) based on anatomic location, seizure subtype, and state of vigilance (SOV). Methods Eleven patients undergoing stereoelectroencephalography for seizure localization were recruited prospectively for local field potential (LFP) recording directly from the ANT. The SOZ was identified using line length and epileptogenicity index. Changes in power spectral density (PSD) were compared between the two anatomic sites as seizures (N = 53) transitioned from interictal baseline to the posttermination stage. Results At baseline, the thalamic LFPs were significantly lower and distinct from the SOZ with the presence of higher power in the fast ripple band (P < 0.001). Temporal changes in ictal power of neural activity within ANT mimic those of the SOZ, are increased significantly at seizure onset (P < 0.05), and are distinct for seizures that impaired awareness or that secondarily generalized (P < 0.05). The onset of seizure was preceded by a decrease in the mean power spectral density (PSD) in ANT and SOZ (P < 0.05). Neural activity correlated with different states of vigilance at seizure onset within the ANT but not in the SOZ (P = 0.005). Interpretation The ANT can be recruited at the onset of mesial temporal lobe seizures, and the recruitment pattern differs with seizure subtypes. Furthermore, changes in neural dynamics precede seizure onset and are widespread to involve temporo‐thalamic regions, thereby providing an opportunity to intervene early with closed‐loop DBS.
Collapse
Affiliation(s)
- Diana Pizarro
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adeel Ilyas
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ganne Chaitanya
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Emilia Toth
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Auriana Irannejad
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Andrew Romeo
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Kristen O Riley
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Leonidas Iasemidis
- Center for Biomedical Engineering and Rehabilitation Science, Louisiana Tech University, Ruston, Louisiana
| | - Sandipan Pati
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| |
Collapse
|