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Jiao D, Xu L, Gu Z, Yan H, Shen D, Gu X. Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies. Neural Regen Res 2025; 20:917-935. [PMID: 38989927 PMCID: PMC11438347 DOI: 10.4103/nrr.nrr-d-23-01444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/31/2023] [Accepted: 01/18/2024] [Indexed: 07/12/2024] Open
Abstract
Epilepsy is a severe, relapsing, and multifactorial neurological disorder. Studies regarding the accurate diagnosis, prognosis, and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy. The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression, protein expression, ion channel activity, energy metabolites, and gut microbiota composition. Satisfactory results are lacking for conventional treatments for epilepsy. Surgical resection of lesions, drug therapy, and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy. Non-pharmacological treatments, such as a ketogenic diet, gene therapy for nerve regeneration, and neural regulation, are currently areas of research focus. This review provides a comprehensive overview of the pathogenesis, diagnostic methods, and treatments of epilepsy. It also elaborates on the theoretical basis, treatment modes, and effects of invasive nerve stimulation in neurotherapy, including percutaneous vagus nerve stimulation, deep brain electrical stimulation, repetitive nerve electrical stimulation, in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation. Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures. Additionally, many new technologies for the diagnosis and treatment of epilepsy are being explored. However, current research is mainly focused on analyzing patients' clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level, which has led to a lack of consensus regarding the mechanisms related to the disease.
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Affiliation(s)
- Dian Jiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lai Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Zhen Gu
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Hua Yan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Dingding Shen
- Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Xiaosong Gu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Khambhati AN, Chang EF, Baud MO, Rao VR. Hippocampal network activity forecasts epileptic seizures. Nat Med 2024; 30:2787-2790. [PMID: 38997606 DOI: 10.1038/s41591-024-03149-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 06/24/2024] [Indexed: 07/14/2024]
Abstract
Seizures in people with epilepsy were long thought to occur at random, but recent methods for seizure forecasting enable estimation of the likelihood of seizure occurrence over short horizons. These methods rely on days-long cyclical patterns of brain electrical activity and other physiological variables that determine seizure likelihood and that require measurement through long-term, multimodal recordings. In this retrospective cohort study of 15 adults with bitemporal epilepsy who had a device that provides chronic intracranial recordings, functional connectivity of hippocampal networks fluctuated in multiday cycles with patterns that mirrored cycles of seizure likelihood. A functional connectivity biomarker of seizure likelihood derived from 90-s recordings of background hippocampal activity generalized across individuals and forecasted 24-h seizure likelihood as accurately as cycle-based models requiring months-long baseline recordings. Larger, prospective studies are needed to validate this approach, but our results have the potential to make reliable seizure forecasts accessible to more people with epilepsy.
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Affiliation(s)
- Ankit N Khambhati
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Maxime O Baud
- Center for Experimental Neurology, Sleep-Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
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3
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Mareš P. Epilepsy Research in the Institute of Physiology of the Czech Academy of Sciences in Prague. Physiol Res 2024; 73:S67-S82. [PMID: 38752773 PMCID: PMC11412343 DOI: 10.33549/physiolres.935391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
Starting from simple clinical statistics, the spectrum of methods used in epilepsy research in the Institute of Physiology of the Czechoslovak (now Czech) Academy of Sciences progressively increased. Professor Servít used electrophysiological methods for study of brain activity in lower vertebrates, neuropathology was focused on electronmicroscopic study of cortical epileptic focus and ion-sensitive microelectrodes were used for studies of cortical direct current potentials. Developmental studies used electrophysiological methods (activity and projection of cortical epileptic foci, EEG under the influence of convulsant drugs, hippocampal, thalamic and cortical electrical stimulation for induction of epileptic afterdischarges and postictal period). Extensive pharmacological studies used seizures elicited by convulsant drugs (at first pentylenetetrazol but also other GABA antagonists as well as agonists of glutamate receptors). Motor performance and behavior were also studied during brain maturation. The last but not least molecular biology was included into the spectrum of methods. Many original data were published making a background of position of our laboratory in the first line of laboratories interested in brain development.
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Affiliation(s)
- P Mareš
- Laboratory of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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4
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Lepeu G, van Maren E, Slabeva K, Friedrichs-Maeder C, Fuchs M, Z'Graggen WJ, Pollo C, Schindler KA, Adamantidis A, Proix T, Baud MO. The critical dynamics of hippocampal seizures. Nat Commun 2024; 15:6945. [PMID: 39138153 PMCID: PMC11322644 DOI: 10.1038/s41467-024-50504-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 07/10/2024] [Indexed: 08/15/2024] Open
Abstract
Epilepsy is defined by the abrupt emergence of harmful seizures, but the nature of these regime shifts remains enigmatic. From the perspective of dynamical systems theory, such critical transitions occur upon inconspicuous perturbations in highly interconnected systems and can be modeled as mathematical bifurcations between alternative regimes. The predictability of critical transitions represents a major challenge, but the theory predicts the appearance of subtle dynamical signatures on the verge of instability. Whether such dynamical signatures can be measured before impending seizures remains uncertain. Here, we verified that predictions on bifurcations applied to the onset of hippocampal seizures, providing concordant results from in silico modeling, optogenetics experiments in male mice and intracranial EEG recordings in human patients with epilepsy. Leveraging pharmacological control over neural excitability, we showed that the boundary between physiological excitability and seizures can be inferred from dynamical signatures passively recorded or actively probed in hippocampal circuits. Of importance for the design of future neurotechnologies, active probing surpassed passive recording to decode underlying levels of neural excitability, notably when assessed from a network of propagating neural responses. Our findings provide a promising approach for predicting and preventing seizures, based on a sound understanding of their dynamics.
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Affiliation(s)
- Gregory Lepeu
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Ellen van Maren
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Kristina Slabeva
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Cecilia Friedrichs-Maeder
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Markus Fuchs
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Werner J Z'Graggen
- Department of Neurosurgery, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Kaspar A Schindler
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Antoine Adamantidis
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Timothée Proix
- Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland
| | - Maxime O Baud
- Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland.
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Pozzilli V, Haggiag S, Di Filippo M, Capone F, Di Lazzaro V, Tortorella C, Gasperini C, Prosperini L. Incidence and determinants of seizures in multiple sclerosis: a meta-analysis of randomised clinical trials. J Neurol Neurosurg Psychiatry 2024; 95:612-619. [PMID: 38383156 DOI: 10.1136/jnnp-2023-332996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/29/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Seizures are reported to be more prevalent in individuals with multiple sclerosis (MS) compared with the general population. Existing data predominantly originate from population-based studies, which introduce variability in methodologies and are vulnerable to selection and reporting biases. METHODS This meta-analysis aims to assess the incidence of seizures in patients participating in randomised clinical trials and to identify potential contributing factors. Data were extracted from 60 articles published from 1993 to 2022. The pooled effect size, representing the incidence rate of seizure events, was estimated using a random-effect model. Metaregression was employed to explore factors influencing the pooled effect size. RESULTS The meta-analysis included data from 53 535 patients and 120 seizure events in a median follow-up of 2 years. The pooled incidence rate of seizures was 68.0 per 100 000 patient-years, significantly higher than the general population rate of 34.6. Generalised tonic-clonic seizures were the most common type reported, although there was a high risk of misclassification for focal seizures with secondary generalisation. Disease progression, longer disease duration, higher disability levels and lower brain volume were associated with a higher incidence of seizures. Particularly, sphingosine-1-phosphate receptor (S1PR) modulators exhibited a 2.45-fold increased risk of seizures compared with placebo or comparators, with a risk difference of 20.5 events per 100 000 patient-years. CONCLUSIONS Patients with MS face a nearly twofold higher seizure risk compared with the general population. This risk appears to be associated not only with disease burden but also with S1PR modulators. Our findings underscore epilepsy as a significant comorbidity in MS and emphasise the necessity for further research into its triggers, preventive measures and treatment strategies.
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Affiliation(s)
- Valeria Pozzilli
- Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Campus Bio-Medico University, Roma, Lazio, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Shalom Haggiag
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Fioravante Capone
- Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Campus Bio-Medico University, Roma, Lazio, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Campus Bio-Medico University, Roma, Lazio, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Carla Tortorella
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
| | - Claudio Gasperini
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
| | - Luca Prosperini
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
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Paschen E, Kleis P, Vieira DM, Heining K, Boehler C, Egert U, Häussler U, Haas CA. On-demand low-frequency stimulation for seizure control: efficacy and behavioural implications. Brain 2024; 147:505-520. [PMID: 37675644 DOI: 10.1093/brain/awad299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/24/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023] Open
Abstract
Mesial temporal lobe epilepsy (MTLE), the most common form of focal epilepsy in adults, is often refractory to medication and associated with hippocampal sclerosis. Deep brain stimulation represents an alternative treatment option for drug-resistant patients who are ineligible for resective brain surgery. In clinical practice, closed-loop stimulation at high frequencies is applied to interrupt ongoing seizures, yet has (i) a high incidence of false detections; (ii) the drawback of delayed seizure-suppressive intervention; and (iii) limited success in sclerotic tissue. As an alternative, low-frequency stimulation (LFS) has been explored recently in patients with focal epilepsies. In preclinical epilepsy models, hippocampal LFS successfully prevented seizures when applied continuously. Since it would be advantageous to reduce the stimulation load, we developed a protocol for on-demand LFS. Given the importance of the hippocampus for navigation and memory, we investigated potential consequences of LFS on hippocampal function. To this end, we used the intrahippocampal kainate mouse model, which recapitulates the key features of MTLE, including spontaneous seizure activity and hippocampal sclerosis. Specifically, our online detection algorithm monitored epileptiform activity in hippocampal local field potential recordings and identified short epileptiform bursts preceding focal seizure clusters, triggering hippocampal LFS to stabilize the network state. To probe behavioural performance, we tested the acute influence of LFS on anxiety-like behaviour in the light-dark box test, spatial and non-spatial memory in the object location memory and novel object recognition test, as well as spatial navigation and long-term memory in the Barnes maze. On-demand LFS was almost as effective as continuous LFS in preventing focal seizure clusters but with a significantly lower stimulation load. When we compared the behavioural performance of chronically epileptic mice to healthy controls, we found that both groups were equally mobile, but epileptic mice displayed an increased anxiety level, altered spatial learning strategy and impaired memory performance. Most importantly, with the application of hippocampal LFS before behavioural training and test sessions, we could rule out deleterious effects on cognition and even show an alleviation of deficits in long-term memory recall in chronically epileptic mice. Taken together, our findings may provide a promising alternative to current therapies, overcoming some of their major limitations, and inspire further investigation of LFS for seizure control in focal epilepsy syndromes.
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Affiliation(s)
- Enya Paschen
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg 79106, Germany
- Faculty of Biology, University of Freiburg, Freiburg 79104, Germany
| | - Piret Kleis
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg 79106, Germany
- Faculty of Biology, University of Freiburg, Freiburg 79104, Germany
| | - Diego M Vieira
- Biomicrotechnology, Department of Microsystems Engineering-IMTEK, Faculty of Engineering, University of Freiburg, Freiburg 79108, Germany
| | - Katharina Heining
- Department of Neuroscience, Karolinska Institutet, Stockholm 17177, Sweden
| | - Christian Boehler
- Department of Microsystems Engineering (IMTEK), Bioelectronic Microtechnology (BEMT), University of Freiburg, Freiburg 79108, Germany
| | - Ulrich Egert
- Biomicrotechnology, Department of Microsystems Engineering-IMTEK, Faculty of Engineering, University of Freiburg, Freiburg 79108, Germany
- BrainLinks-BrainTools Center, University of Freiburg, Freiburg 79110, Germany
| | - Ute Häussler
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg 79106, Germany
- BrainLinks-BrainTools Center, University of Freiburg, Freiburg 79110, Germany
| | - Carola A Haas
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg 79106, Germany
- BrainLinks-BrainTools Center, University of Freiburg, Freiburg 79110, Germany
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7
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Pokošová P, Kala D, Šanda J, Ježdík P, Prysiazhniuk Y, Faridová A, Jahodová A, Bělohlávková A, Kalina A, Holubová Z, Jurášek B, Kynčl M, Otáhal J. Magnetic resonance imaging techniques for indirect assessment of myelin content in the brain using standard T1w and T2w MRI sequences and postprocessing analysis. Physiol Res 2023; 72:S573-S585. [PMID: 38165761 PMCID: PMC10861246 DOI: 10.33549/physiolres.935250] [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: 03/31/2023] [Accepted: 10/10/2023] [Indexed: 02/01/2024] Open
Abstract
Magnetic Resonance Imaging (MRI) has revolutionized our ability to non-invasively study the brain's structural and functional properties. However, detecting myelin, a crucial component of white matter, remains challenging due to its indirect visibility on conventional MRI scans. Myelin plays a vital role in neural signal transmission and is associated with various neurological conditions. Understanding myelin distribution and content is crucial for insights into brain development, aging, and neurological disorders. Although specialized MRI sequences can estimate myelin content, these are time-consuming. Also, many patients sent to specialized neurological centers have an MRI of the brain already scanned. In this study, we focused on techniques utilizing standard MRI T1-weighted (T1w) and T2 weighted (T2w) sequences commonly used in brain imaging protocols. We evaluated the applicability of the T1w/T2w ratio in assessing myelin content by comparing it to quantitative T1 mapping (qT1). Our study included 1 healthy adult control and 7 neurologic patients (comprising both pediatric and adult populations) with epilepsy originating from focal epileptogenic lesions visible on MRI structural scans. Following image acquisition on a 3T Siemens Vida scanner, datasets were co registered, and segmented into anatomical regions using the Fastsurfer toolbox, and T1w/T2w ratio maps were calculated in Matlab software. We further assessed interhemispheric differences in volumes of individual structures, their signal intensity, and the correlation of the T1w/T2w ratio to qT1. Our data demonstrate that in situations where a dedicated myelin-sensing sequence such as qT1 is not available, the T1w/T2w ratio provides significantly better information than T1w alone. By providing indirect information about myelin content, this technique offers a valuable tool for understanding the neurobiology of myelin-related conditions using basic brain scans.
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Affiliation(s)
- P Pokošová
- Department of Pathophysiology, Second Faculty of Medicine, Charles University, Praha 5, Czech Republic.
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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9
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Jiruska P, Freestone D, Gnatkovsky V, Wang Y. An update on the seizures beget seizures theory. Epilepsia 2023; 64 Suppl 3:S13-S24. [PMID: 37466948 DOI: 10.1111/epi.17721] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 07/20/2023]
Abstract
Seizures beget seizures is a longstanding theory that proposed that seizure activity can impact the structural and functional properties of the brain circuits in ways that contribute to epilepsy progression and the future occurrence of seizures. Originally proposed by Gowers, this theory continues to be quoted in the pathophysiology of epilepsy. We critically review the existing data and observations on the consequences of recurrent seizures on brain networks and highlight a range of factors that speak for and against the theory. The existing literature demonstrates clearly that ictal activity, especially if recurrent, induces molecular, structural, and functional changes including cell loss, connectivity reorganization, changes in neuronal behavior, and metabolic alterations. These changes have the potential to modify the seizure threshold, contribute to disease progression, and recruit wider areas of the epileptic network into epileptic activity. Repeated seizure activity may, thus, act as a pathological positive-feedback mechanism that increases seizure likelihood. On the other hand, the time course of self-limited epilepsies and the presence of seizure remission in two thirds of epilepsy cases and various chronic epilepsy models oppose the theory. Experimental work showed that seizures could induce neural changes that increase the seizure threshold and decrease the risk of a subsequent seizure. Due to the complex nature of epilepsies, it is wrong to consider only seizures as the key factor responsible for disease progression. Epilepsy worsening can be attributed to the various forms of interictal epileptiform activity or underlying disease mechanisms. Although seizure activity can negatively impact brain structure and function, the "seizures beget seizures" theory should not be used dogmatically but with extreme caution.
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Affiliation(s)
- Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | | | - Vadym Gnatkovsky
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Yujiang Wang
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
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Sun F, Wang Y, Li Y, Li Y, Wang S, Xu F, Wang X. Variation in functional networks between clinical and subclinical discharges in childhood absence epilepsy: A multi-frequency MEG study. Seizure 2023; 111:109-121. [PMID: 37598560 DOI: 10.1016/j.seizure.2023.08.005] [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: 04/24/2023] [Revised: 08/06/2023] [Accepted: 08/09/2023] [Indexed: 08/22/2023] Open
Abstract
OBJECTIVE Two types of spike-and-wave discharges (SWDs) exist in childhood absence epilepsy (CAE): clinical discharges are prolonged and manifest primarily as impaired consciousness, whereas subclinical discharges are brief with few objectively visible symptoms. This study aimed to compare neural functional network and default mode network (DMN) activity between clinical and subclinical discharges to better understand the underlying mechanism of CAE. METHODS Using magnetoencephalography (MEG) data from 21 patients, we obtained 25 segments each of clinical discharges and subclinical discharges. Amplitude envelope correlation analysis was used to construct functional networks and graph theory was used to calculate network topological data. We then compared differences in functional connectivity within the DMN between clinical and subclinical discharges. All statistical comparisons were performed using paired-sample tests. RESULTS Compared to subclinical discharges, the functional network of clinical discharges exhibited higher synchronization - particularly in the parahippocampal gyrus - as early as 10 s before the seizure. Additionally, the functional network of clinical SWDs presented an anterior shift of key nodes in the alpha frequency band. Regarding clinical discharge progression, there were gradual increases in the parameter node strengths (S), clustering coefficients (C), and global efficiency (E) of the functional networks, while the path lengths (L) decreased. These changes were most prominent at the onset of discharges and followed by some recovery in the high-frequency bands, but no significant change in the low-frequency bands. Furthermore, connections within the DMN during the discharge period were significantly stronger for clinical discharge compared to subclinical discharges. CONCLUSIONS These findings suggest that a more regular network before abnormal discharges in clinical discharges contributes to SWD explosion and that the parahippocampal gyrus plays an important role in maintaining oscillations. An absence seizure is a gradual process and the emergence of SWDs may be accompanied by initiation of inhibitory mechanisms. Enhanced functional connectivity among DMN brain regions may indicate that patients have entered a state of introspection, and functional abnormalities in the parahippocampal gyrus may be associated with patients' transient memory loss.
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Affiliation(s)
- Fangling Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingfan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yanzhang Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Siyi Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Fengyuan Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [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: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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12
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Fukushima K, Higashiyama H, Kazuta Y, Hashimoto K, Watanabe N, Furuya Y, Ito Y, Wu T, Kosasa T, Talos DM, Song Y, Roberts NS, Jensen FE, Hanada T, Ido K. Discovery of E2730, a novel selective uncompetitive GAT1 inhibitor, as a candidate for anti-seizure medication. Epilepsia Open 2023; 8:834-845. [PMID: 37052238 PMCID: PMC10472371 DOI: 10.1002/epi4.12741] [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: 12/12/2022] [Accepted: 04/08/2023] [Indexed: 04/14/2023] Open
Abstract
OBJECTIVE As of 2022, 36 anti-seizure medications (ASMs) have been licensed for the treatment of epilepsy, however, adverse effects (AEs) are commonly reported. Therefore, ASMs with a wide margin between therapeutic effects and AEs are preferred over ASMs that are associated with a narrow margin between efficacy and risk of AEs. E2730 was discovered using in vivo phenotypic screening and characterized as an uncompetitive, yet selective, inhibitor of γ-aminobutyric acid (GABA) transporter 1 (GAT1). Here, we describe the preclinical characteristics of E2730. METHODS Anti-seizure effects of E2730 were evaluated in several animal models of epilepsy: corneal kindling, 6 Hz-44 mA psychomotor seizure, amygdala kindling, Fragile X syndrome, and Dravet syndrome models. Effects of E2730 on motor coordination were assessed in accelerating rotarod tests. The mechanism of action of E2730 was explored by [3 H]E2730 binding assay. The GAT1-selectivity over other GABA transporters was examined by GABA uptake assay of GAT1, GAT2, GAT3, or betaine/GABA transporter 1 (BGT-1) stably expressing HEK293 cells. To further investigate the mechanism for E2730-mediated inhibition of GAT1, in vivo microdialysis and in vitro GABA uptake assays were conducted under conditions of different GABA concentrations. RESULTS E2730 showed anti-seizure effects in the assessed animal models with an approximately >20-fold margin between efficacy and motor incoordination. [3 H]E2730 binding on brain synaptosomal membrane was abolished in GAT1-deficient mice, and E2730 selectively inhibited GAT1-mediated GABA uptake over other GABA transporters. In addition, results of GABA uptake assays showed that E2730-mediated inhibition of GAT1 positively correlated to the level of ambient GABA in vitro. E2730 also increased extracellular GABA concentration in hyperactivated conditions but not under basal levels in vivo. SIGNIFICANCE E2730 is a novel, selective, uncompetitive GAT1 inhibitor, which acts selectively under the condition of increasing synaptic activity, contributing to a wide margin between therapeutic effect and motor incoordination.
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Affiliation(s)
| | | | - Yuji Kazuta
- Deep Human Biology LearningEisai Co., Ltd.TsukubaIbarakiJapan
| | | | - Naoto Watanabe
- Deep Human Biology LearningEisai Co., Ltd.TsukubaIbarakiJapan
| | - Yoshiaki Furuya
- Deep Human Biology LearningEisai Co., Ltd.TsukubaIbarakiJapan
| | - Yoshimasa Ito
- Neurology Business GroupEisai Co., Ltd.TsukubaIbarakiJapan
| | - Ting Wu
- Alzheimer's Disease and Brain HealthEisai Co., Ltd.TsukubaIbarakiJapan
| | - Takashi Kosasa
- Neurology Business GroupEisai Co., Ltd.TsukubaIbarakiJapan
| | - Delia M. Talos
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Yeri Song
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Nicholas S. Roberts
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Frances E. Jensen
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Takahisa Hanada
- Deep Human Biology LearningEisai Co., Ltd.TsukubaIbarakiJapan
| | - Katsutoshi Ido
- Neurology Business GroupEisai Co., Ltd.TsukubaIbarakiJapan
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13
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Dallmer-Zerbe I, Jajcay N, Chvojka J, Janca R, Jezdik P, Krsek P, Marusic P, Jiruska P, Hlinka J. Computational modeling allows unsupervised classification of epileptic brain states across species. Sci Rep 2023; 13:13436. [PMID: 37596382 PMCID: PMC10439162 DOI: 10.1038/s41598-023-39867-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023] Open
Abstract
Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Nikola Jajcay
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- National Institute of Mental Health, 250 67, Klecany, Czech Republic
| | - Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Radek Janca
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Petr Jezdik
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Pavel Krsek
- Department of Paediatric Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic.
- National Institute of Mental Health, 250 67, Klecany, Czech Republic.
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14
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Graham RT, Parrish RR, Alberio L, Johnson EL, Owens L, Trevelyan AJ. Optogenetic stimulation reveals a latent tipping point in cortical networks during ictogenesis. Brain 2023; 146:2814-2827. [PMID: 36572952 PMCID: PMC10316782 DOI: 10.1093/brain/awac487] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/17/2022] [Accepted: 12/06/2022] [Indexed: 12/28/2022] Open
Abstract
Brain-state transitions are readily apparent from changes in brain rhythms,1 but are difficult to predict, suggestive that the underlying cause is latent to passive recording methods. Among the most important transitions, clinically, are the starts of seizures. We here show that an 'active probing' approach may have several important benefits for epileptic management, including by helping predict these transitions. We used mice expressing the optogenetic actuator, channelrhodopsin, in pyramidal cells, allowing this population to be stimulated in isolation. Intermittent stimulation at frequencies as low as 0.033 Hz (period = 30 s) delayed the onset of seizure-like events in an acute brain slice model of ictogenesis, but the effect was lost if stimulation was delivered at even lower frequencies (1/min). Notably, active probing additionally provides advance indication of when seizure-like activity is imminent, revealed by monitoring the postsynaptic response to stimulation. The postsynaptic response, recorded extracellularly, showed an all-or-nothing change in both amplitude and duration, a few hundred seconds before seizure-like activity began-a sufficient length of time to provide a helpful warning of an impending seizure. The change in the postsynaptic response then persisted for the remainder of the recording, indicative of a state change from a pre-epileptic to a pro-epileptic network. This occurred in parallel with a large increase in the stimulation-triggered Ca2+ entry into pyramidal dendrites, and a step increase in the number of evoked postsynaptic action potentials, both consistent with a reduction in the threshold for dendritic action potentials. In 0 Mg2+ bathing media, the reduced threshold was not associated with changes in glutamatergic synaptic function, nor of GABAergic release from either parvalbumin or somatostatin interneurons, but simulations indicate that the step change in the optogenetic response can instead arise from incremental increases in intracellular [Cl-]. The change in the response to stimulation was replicated by artificially raising intracellular [Cl-], using the optogenetic chloride pump, halorhodopsin. By contrast, increases in extracellular [K+] cannot account for the firing patterns in the response to stimulation, although this, and other cellular changes, may contribute to ictal initiation in other circumstances. We describe how these various cellular changes form a synergistic network of positive feedback mechanisms, which may explain the precipitous nature of seizure onset. This model of seizure initiation draws together several major lines of epilepsy research as well as providing an important proof-of-principle regarding the utility of open-loop brain stimulation for clinical management of the condition.
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Affiliation(s)
- Robert T Graham
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - R Ryley Parrish
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Laura Alberio
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Emily L Johnson
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Laura Owens
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Andrew J Trevelyan
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
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15
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Janca R, Jezdik P, Ebel M, Kalina A, Kudr M, Jahodova A, Krysl D, Mackova K, Straka B, Marusic P, Krsek P. Distinct patterns of interictal intracranial EEG in focal cortical dysplasia type I and II. Clin Neurophysiol 2023; 151:10-17. [PMID: 37121217 DOI: 10.1016/j.clinph.2023.03.360] [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: 12/28/2022] [Revised: 03/13/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling. METHODS This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types. RESULTS Patients with FCD type II had a significantly higher IED rate (p < 0.005), a shorter inter-discharge interval within RD episodes (p < 0.003), sleep influence on decreased RD periodicity (p < 0.036), and longer RD episode duration (p < 0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy. CONCLUSION Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II. SIGNIFICANCE Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.
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Affiliation(s)
- Radek Janca
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic.
| | - Petr Jezdik
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Matyas Ebel
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Adam Kalina
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Martin Kudr
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Alena Jahodova
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - David Krysl
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Katerina Mackova
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Barbora Straka
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Petr Marusic
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Pavel Krsek
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
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16
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Liu S, Li F, Wan F. Distance to Criticality Undergoes Critical Transition Before Epileptic Seizure Attacks. Brain Res Bull 2023:110684. [PMID: 37353038 DOI: 10.1016/j.brainresbull.2023.110684] [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: 05/15/2023] [Revised: 06/03/2023] [Accepted: 06/10/2023] [Indexed: 06/25/2023]
Abstract
Epilepsy is a common neurological disorder characterized by recurring seizures, but its underlying mechanisms remain poorly understood. Despite extensive research, there are still gaps in our knowledge about the relationship between brain dynamics and seizures. In this study, our aim is to address these gaps by proposing a novel approach to assess the role of brain network dynamics in the onset of seizures. Specifically, we investigate the relationship between brain dynamics and seizures by tracking the distance to criticality. Our hypothesis is that this distance plays a crucial role in brain state changes and that seizures may be related to critical transitions of this distance. To test this hypothesis, we develop a method to measure the evolution of the brain network's distance to the critical dynamic systems (i.e., the distance to the tipping point, DTP) using dynamic network biomarker theory and random matrix theory. The results show that the DTP of the brain decreases significantly immediately after onset of an epileptic seizure, suggesting that the brain loses its well-defined quasi-critical state during seizures. We refer to this phenomenon as the "criticality of the criticality" (COC). Furthermore, we observe that DTP exhibits a shape transition before and after the onset of the seizures. This phenomenon suggests the possibility of early warning signal (EWS) identification in the dynamic sequence of DTP, which could be utilized for seizure prediction. Our results show that the Hurst exponent, skewness, kurtosis, autocorrelation, and variance of the DTP sequence are potential EWS features. This study advances our understanding of the relationship between brain dynamics and seizures and highlights the potential for using criticality-based measures to predict and prevent seizures.
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Affiliation(s)
- Shun Liu
- The Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau; The Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau; The Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau
| | - Fali Li
- The Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuro-information, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, the Center for Information in Bio-Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Wan
- The Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau; The Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau; The Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau.
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17
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Lai N, Li Z, Xu C, Wang Y, Chen Z. Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy. Neurobiol Dis 2023; 177:105999. [PMID: 36638892 DOI: 10.1016/j.nbd.2023.105999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.
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Affiliation(s)
- Nanxi Lai
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhisheng Li
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China; Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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18
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Trevelyan AJ, Graham RT, Parrish RR, Codadu NK. Synergistic Positive Feedback Mechanisms Underlying Seizure Initiation. Epilepsy Curr 2023; 23:38-43. [PMID: 36923333 PMCID: PMC10009126 DOI: 10.1177/15357597221127163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Investigations into seizure initiation, in recent years, have focused almost entirely upon alterations of interneuronal function, chloride homeostasis, and extracellular potassium levels. In contrast, little attention has been directed toward a possible role of dendritic plateau potentials in the actual ictogenic transition, despite a substantial literature dating back 40 years regarding its importance generally in epilepsy. Here, we argue that an increase in dendritic excitability, coordinated across the population of pyramidal cells, is a key stage in ictogenesis.
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Affiliation(s)
- Andrew J. Trevelyan
- Newcastle University Biosciences Institute, Medical School, Framlington Place, Newcastle upon Tyne, United Kingdom
| | - Robert T. Graham
- Queen Square Institute of Neurology, University College London, United Kingdom
| | - R. Ryley Parrish
- Department of Cell Biology and Physiology, Brigham Young University, Provo, UT, USA
| | - Neela K. Codadu
- Queen Square Institute of Neurology, University College London, United Kingdom
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19
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Xiong W, Kameneva T, Lambert E, Cook MJ, Richardson MP, Nurse ES. Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG. J Neural Eng 2022; 19. [PMID: 36270501 DOI: 10.1088/1741-2552/ac9c97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
Objective.Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES).Approach.Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC).Main results.It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79.Significance.To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.
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Affiliation(s)
- Wenjuan Xiong
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Tatiana Kameneva
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.,Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Elisabeth Lambert
- Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Ewan S Nurse
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Seer Medical, Melbourne, Australia
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20
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Zhai SR, Ehrens D, Li A, Assaf F, Schiller Y, Sarma SV, Smith RJ. Temporal and morphological characteristics of high-frequency oscillations in an acute in vivo model of epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4896-4899. [PMID: 36086062 DOI: 10.1109/embc48229.2022.9871323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Approximately 30% of patients with epilepsy do not respond to anti-epileptogenic drugs. Surgical removal of the epileptogenic zone (EZ), the brain regions where the seizures originate and spread, can be a possible therapy for these patients, but localizing the EZ is challenging due to a variety of clinical factors. High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the EZ, but it is currently unknown whether HFO rates and HFO morphology modulate as pathological brain networks evolve in a way that gives rise to seizures. To address this question, we assessed the temporal evolution of the duration of HFO events, amplitude of HFO events, and rates of HFOs per minute. HFO events were quantified using the 4AP in vivo rodent model of epilepsy, inducing seizures in two different brain areas. We found that the duration and amplitude of HFO events were significantly increased for the cortex model when compared to the hippocampus model. Additionally, the duration and amplitude increased significantly between baseline and pre-ictal HFOs in both models. On the other hand, the two models did not display a consistent increasing or decreasing trend in amplitude, duration or rate when comparing ictal and postictal intervals. Clinical Relevance- We assessed the amplitude, duration, and rate of HFOs in two acute in vivo rodent models of epilepsy. The significant modulation of HFO morphology from baseline to pre-ictal periods suggests that these features may be a robust biomarker for pathological tissue involved in epileptogenesis. Moreover, the differences in HFO morphology observed between cortex and hippocampus animal models possibly indicate that different structural network characteristics of the EZ cause this modulation. In all, we found that HFO features modulate significantly with the onset of seizures, further highlighting the need to consider of HFO morphology in EZ-localizing studies.
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21
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John TT, Ahmed OJ. Finding a Fragile Piece to End the Seizure War. Epilepsy Curr 2022; 22:178-180. [DOI: 10.1177/15357597221094937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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22
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Gregg NM, Sladky V, Nejedly P, Mivalt F, Kim I, Balzekas I, Sturges BK, Crowe C, Patterson EE, Van Gompel JJ, Lundstrom BN, Leyde K, Denison TJ, Brinkmann BH, Kremen V, Worrell GA. Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy. Sci Rep 2021; 11:24250. [PMID: 34930926 PMCID: PMC8688461 DOI: 10.1038/s41598-021-03555-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/03/2021] [Indexed: 11/30/2022] Open
Abstract
Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.
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Affiliation(s)
- Nicholas M Gregg
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Vladimir Sladky
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- International Clinical Research Center, St. Anne's University Hospital, 656 91, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, 272 01, Kladno, Czech Republic
| | - Petr Nejedly
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- International Clinical Research Center, St. Anne's University Hospital, 656 91, Brno, Czech Republic
| | - Filip Mivalt
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- International Clinical Research Center, St. Anne's University Hospital, 656 91, Brno, Czech Republic
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, 616 00, Brno, Czech Republic
| | - Inyong Kim
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
| | - Irena Balzekas
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- Mayo Clinic School of Medicine and the Medical Scientist Training Program, Mayo Clinic, Rochester, MN, 55905, USA
| | - Beverly K Sturges
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, 95616, USA
| | - Chelsea Crowe
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, 95616, USA
| | - Edward E Patterson
- Department of Veterinary Clinical Sciences, University of Minnesota College of Veterinary Medicine, St. Paul, MN, 55108, USA
| | | | - Brian N Lundstrom
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kent Leyde
- Cadence Neuroscience, Seattle, WA, 98052, USA
| | - Timothy J Denison
- Institute for Biomedical Engineering, Oxford University, Oxford, OX3 7DQ, UK
| | - Benjamin H Brinkmann
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
| | - Vaclav Kremen
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, 160 00, Prague, Czech Republic
| | - Gregory A Worrell
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
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23
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Lee WJ, Moon J, Lim JA, Jeon D, Yoo JS, Park DK, Han D, Lee ST, Jung KH, Park KI, Lee SK, Chu K. Proteins related to ictogenesis and seizure clustering in chronic epilepsy. Sci Rep 2021; 11:21508. [PMID: 34728717 PMCID: PMC8563854 DOI: 10.1038/s41598-021-00956-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/14/2021] [Indexed: 12/01/2022] Open
Abstract
Seizure clustering is a common phenomenon in epilepsy. Protein expression profiles during a seizure cluster might reflect the pathomechanism underlying ictogenesis. We performed proteomic analyses to identify proteins with a specific temporal expression pattern in cluster phases and to demonstrate their potential pathomechanistic role. Pilocarpine epilepsy model mice with confirmed cluster pattern of spontaneous recurrent seizures by long-term video-electroencpehalography were sacrificed at the onset, peak, or end of a seizure cluster or in the seizure-free period. Proteomic analysis was performed in the hippocampus and the cortex. Differentially expressed proteins (DEPs) were identified and classified according to their temporal expression pattern. Among the five hippocampal (HC)-DEP classes, HC-class 1 (66 DEPs) represented disrupted cell homeostasis due to clustered seizures, HC-class 2 (63 DEPs) cluster-onset downregulated processes, HC-class 3 (42 DEPs) cluster-onset upregulated processes, and HC-class 4 (103 DEPs) consequences of clustered seizures. Especially, DEPs in HC-class 3 were hippocampus-specific and involved in axonogenesis, synaptic vesicle assembly, and neuronal projection, indicating their pathomechanistic roles in ictogenesis. Key proteins in HC-class 3 were highly interconnected and abundantly involved in those biological processes. This study described the seizure cluster-associated spatiotemporal regulation of protein expression. HC-class 3 provides insights regarding ictogenesis-related processes.
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Affiliation(s)
- Woo-Jin Lee
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Program in Neuroscience, Neuroscience Research Institute of SNUMRC, Seoul National University College of Medicine, Seoul, South Korea
| | - Jangsup Moon
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Program in Neuroscience, Neuroscience Research Institute of SNUMRC, Seoul National University College of Medicine, Seoul, South Korea
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jung-Ah Lim
- Department of Neurology, Cham Joeun Hospital, Gwangju, South Korea
| | - Daejong Jeon
- Advanced Neural Technologies, Seoul, South Korea
| | - Jung-Suk Yoo
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Dong-Kyu Park
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Dohyun Han
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea
| | - Soon-Tae Lee
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Program in Neuroscience, Neuroscience Research Institute of SNUMRC, Seoul National University College of Medicine, Seoul, South Korea
| | - Keun-Hwa Jung
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Program in Neuroscience, Neuroscience Research Institute of SNUMRC, Seoul National University College of Medicine, Seoul, South Korea
| | - Kyung-Il Park
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Program in Neuroscience, Neuroscience Research Institute of SNUMRC, Seoul National University College of Medicine, Seoul, South Korea
- Department of Neurology, Seoul National University Healthcare System Gangnam Center, Seoul, South Korea
| | - Sang Kun Lee
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
- Program in Neuroscience, Neuroscience Research Institute of SNUMRC, Seoul National University College of Medicine, Seoul, South Korea.
| | - Kon Chu
- Department of Neurology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
- Program in Neuroscience, Neuroscience Research Institute of SNUMRC, Seoul National University College of Medicine, Seoul, South Korea.
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24
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Hubbard I, Beniczky S, Ryvlin P. The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here. Front Neurol 2021; 12:740743. [PMID: 34659099 PMCID: PMC8517120 DOI: 10.3389/fneur.2021.740743] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
Seizure detection, and more recently seizure forecasting, represent important avenues of clinical development in epilepsy, promoted by progress in wearable devices and mobile health (mHealth), which might help optimizing seizure control and prevention of seizure-related mortality and morbidity in persons with epilepsy. Yet, very long-term continuous monitoring of seizure-sensitive biosignals in the ambulatory setting presents a number of challenges. We herein provide an overview of these challenges and current technological landscape of mHealth devices for seizure detection. Specifically, we display, which types of sensor modalities and analytical methods are available, and give insight into current clinical practice guidelines, main outcomes of clinical validation studies, and discuss how to evaluate device performance at point-of-care facilities. We then address pitfalls which may arise in patient compliance and the need to design solutions adapted to user experience.
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Affiliation(s)
- Ilona Hubbard
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
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25
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Li A, Huynh C, Fitzgerald Z, Cajigas I, Brusko D, Jagid J, Claudio AO, Kanner AM, Hopp J, Chen S, Haagensen J, Johnson E, Anderson W, Crone N, Inati S, Zaghloul KA, Bulacio J, Gonzalez-Martinez J, Sarma SV. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci 2021; 24:1465-1474. [PMID: 34354282 PMCID: PMC8547387 DOI: 10.1038/s41593-021-00901-w] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
Over 15 million patients with epilepsy worldwide do not respond to drugs. Successful surgical treatment requires complete removal or disconnection of the seizure onset zone (SOZ), brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30 and 70% because no clinically validated biological marker of the SOZ exists. We develop and retrospectively validate a new electroencephalogram (EEG) marker-neural fragility-in a retrospective analysis of 91 patients by using neural fragility of the annotated SOZ as a metric to predict surgical outcomes. Fragility predicts 43 out of 47 surgical failures, with an overall prediction accuracy of 76% compared with the accuracy of clinicians at 48% (successful outcomes). In failed outcomes, we identify fragile regions that were untreated. When compared to 20 EEG features proposed as SOZ markers, fragility outperformed in predictive power and interpretability, which suggests neural fragility as an EEG biomarker of the SOZ.
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Affiliation(s)
- Adam Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
| | - Chester Huynh
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | | | - Iahn Cajigas
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Damian Brusko
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jonathan Jagid
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Angel O Claudio
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Andres M Kanner
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jennifer Hopp
- Neurology, University of Maryland Medical Center, Baltimore, MD, USA
| | - Stephanie Chen
- Neurology, University of Maryland Medical Center, Baltimore, MD, USA
| | | | - Emily Johnson
- Neurology, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Nathan Crone
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Neurology, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Sara Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Juan Bulacio
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Jorge Gonzalez-Martinez
- Neurosurgery and Epilepsy Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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26
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Köksal Ersöz E, Wendling F. Canard solutions in neural mass models: consequences on critical regimes. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2021; 11:11. [PMID: 34529192 PMCID: PMC8446153 DOI: 10.1186/s13408-021-00109-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 08/17/2021] [Indexed: 05/06/2023]
Abstract
Mathematical models at multiple temporal and spatial scales can unveil the fundamental mechanisms of critical transitions in brain activities. Neural mass models (NMMs) consider the average temporal dynamics of interconnected neuronal subpopulations without explicitly representing the underlying cellular activity. The mesoscopic level offered by the neural mass formulation has been used to model electroencephalographic (EEG) recordings and to investigate various cerebral mechanisms, such as the generation of physiological and pathological brain activities. In this work, we consider a NMM widely accepted in the context of epilepsy, which includes four interacting neuronal subpopulations with different synaptic kinetics. Due to the resulting three-time-scale structure, the model yields complex oscillations of relaxation and bursting types. By applying the principles of geometric singular perturbation theory, we unveil the existence of the canard solutions and detail how they organize the complex oscillations and excitability properties of the model. In particular, we show that boundaries between pathological epileptic discharges and physiological background activity are determined by the canard solutions. Finally we report the existence of canard-mediated small-amplitude frequency-specific oscillations in simulated local field potentials for decreased inhibition conditions. Interestingly, such oscillations are actually observed in intracerebral EEG signals recorded in epileptic patients during pre-ictal periods, close to seizure onsets.
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Affiliation(s)
- Elif Köksal Ersöz
- Univ Rennes, INSERM, LTSI-U1099, Campus de Beaulieu, F - 35000, Rennes, France
| | - Fabrice Wendling
- Univ Rennes, INSERM, LTSI-U1099, Campus de Beaulieu, F - 35000, Rennes, France.
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27
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Hadjiabadi D, Lovett-Barron M, Raikov IG, Sparks FT, Liao Z, Baraban SC, Leskovec J, Losonczy A, Deisseroth K, Soltesz I. Maximally selective single-cell target for circuit control in epilepsy models. Neuron 2021; 109:2556-2572.e6. [PMID: 34197732 PMCID: PMC8448204 DOI: 10.1016/j.neuron.2021.06.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 04/19/2021] [Accepted: 06/04/2021] [Indexed: 12/21/2022]
Abstract
Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
| | - Matthew Lovett-Barron
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Fraser T Sparks
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Scott C Baraban
- Department of Neurological Surgery and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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28
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Chvojka J, Kudlacek J, Chang WC, Novak O, Tomaska F, Otahal J, Jefferys JGR, Jiruska P. The role of interictal discharges in ictogenesis - A dynamical perspective. Epilepsy Behav 2021; 121:106591. [PMID: 31806490 DOI: 10.1016/j.yebeh.2019.106591] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 09/23/2019] [Accepted: 09/23/2019] [Indexed: 10/25/2022]
Abstract
Interictal epileptiform discharge (IED) is a traditional hallmark of epileptic tissue that is generated by the synchronous activity of a population of neurons. Interictal epileptiform discharges represent a heterogeneous group of pathological activities that differ in shape, duration, spatiotemporal distribution, underlying cellular and network mechanisms, and their relationship to seizure genesis. The exact role of IEDs in epilepsy is still not well understood, and there remains a persistent dichotomy about the impact on IEDs on seizures. Proseizure, antiseizure, and no impact on ictogenesis have all been described in previous studies. In this article, we review the existing knowledge on the role of interictal discharges in seizure genesis, and we discuss how dynamical approaches to ictogenesis can explain the existing dichotomy about the multifaceted role of IEDs in ictogenesis. This article is part of the Special Issue "NEWroscience 2018".
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Affiliation(s)
- Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Kudlacek
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Wei-Chih Chang
- Faculty of Veterinary Medicine and Neuroscience Center, University of Helsinki, Helsinki 00014, Finland
| | - Ondrej Novak
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Filip Tomaska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jakub Otahal
- Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - John G R Jefferys
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Pharmacology, University of Oxford, Mansfield Road, Oxford OX1 3QT, United Kingdom
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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29
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Xiong W, Nurse ES, Lambert E, Cook MJ, Kameneva T. Seizure Forecasting Using Long-Term Electroencephalography and Electrocardiogram Data. Int J Neural Syst 2021; 31:2150039. [PMID: 34334122 DOI: 10.1142/s0129065721500398] [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] [Indexed: 11/18/2022]
Abstract
Electroencephalography (EEG) has been used to forecast seizures with varying success. There is an increasing interest to use electrocardiogram (ECG) to help with seizure forecasting. The neural and cardiovascular systems may exhibit critical slowing, which is measured by an increase in variance and autocorrelation of the system, when change from a normal state to an ictal state. To forecast seizures, the variance and autocorrelation of long-term continuous EEG and ECG data from 16 patients were used for analysis. The average period of recordings was 161.9 h, with an average of 9 electrographic seizures in an individual patient. The relationship between seizure onset times and phases of variance and autocorrelation in EEG and ECG data was investigated. The results of forecasting models using critical slowing features, seizure circadian features, and combined critical slowing and circadian features were compared using the receiver-operating characteristic curve. The results demonstrated that the best forecaster was patient-specific and the average area under the curve (AUC) of the best forecaster across patients was 0.68. In 50% of patients, circadian forecasters had the best performance. Critical slowing forecaster performed best in 19% of patients. Combined forecaster achieved the best performance in 31% of patients. The results of this study may help to advance the field of seizure forecasting and lead to the improved quality of life of people who suffer from epilepsy.
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Affiliation(s)
- Wenjuan Xiong
- School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia
| | | | - Elisabeth Lambert
- School of Health Sciences Swinburne, University of Technology, Melbourne, Australia.,Iverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Tatiana Kameneva
- School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia.,Iverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
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30
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Huang W, Ke Y, Zhu J, Liu S, Cong J, Ye H, Guo Y, Wang K, Zhang Z, Meng W, Gao TM, Luhmann HJ, Kilb W, Chen R. TRESK channel contributes to depolarization-induced shunting inhibition and modulates epileptic seizures. Cell Rep 2021; 36:109404. [PMID: 34289346 DOI: 10.1016/j.celrep.2021.109404] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 03/19/2021] [Accepted: 06/23/2021] [Indexed: 11/18/2022] Open
Abstract
Glutamatergic and GABAergic synaptic transmission controls excitation and inhibition of postsynaptic neurons, whereas activity of ion channels modulates neuronal intrinsic excitability. However, it is unclear how excessive neuronal excitation affects intrinsic inhibition to regain homeostatic stability under physiological or pathophysiological conditions. Here, we report that a seizure-like sustained depolarization can induce short-term inhibition of hippocampal CA3 neurons via a mechanism of membrane shunting. This depolarization-induced shunting inhibition (DShI) mediates a non-synaptic, but neuronal intrinsic, short-term plasticity that is able to suppress action potential generation and postsynaptic responses by activated ionotropic receptors. We demonstrate that the TRESK channel significantly contributes to DShI. Disruption of DShI by genetic knockout of TRESK exacerbates the sensitivity and severity of epileptic seizures of mice, whereas overexpression of TRESK attenuates seizures. In summary, these results uncover a type of homeostatic intrinsic plasticity and its underlying mechanism. TRESK might represent a therapeutic target for antiepileptic drugs.
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Affiliation(s)
- Weiyuan Huang
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yue Ke
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jianping Zhu
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Shuai Liu
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jin Cong
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Hailin Ye
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yanwu Guo
- The National Key Clinic Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Kewan Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Zhenhai Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Center for Precision Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510030, China; Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
| | - Wenxiang Meng
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Tian-Ming Gao
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China; State Key Laboratory of Organ Failure Research, Collaborative Innovation Center for Brain Science, Southern Medical University, Guangzhou 510515, China
| | - Heiko J Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, Mainz 55120, Germany
| | - Werner Kilb
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, Mainz 55120, Germany.
| | - Rongqing Chen
- Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; The National Key Clinic Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China; Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China.
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31
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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.
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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
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32
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Gupta K, Paluš M. Cross-Scale Causality and Information Transfer in Simulated Epileptic Seizures. ENTROPY 2021; 23:e23050526. [PMID: 33923035 PMCID: PMC8146730 DOI: 10.3390/e23050526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 12/04/2022]
Abstract
An information-theoretic approach for detecting causality and information transfer was applied to phases and amplitudes of oscillatory components related to different time scales and obtained using the wavelet transform from a time series generated by the Epileptor model. Three main time scales and their causal interactions were identified in the simulated epileptic seizures, in agreement with the interactions of the model variables. An approach consisting of wavelet transform, conditional mutual information estimation, and surrogate data testing applied to a single time series generated by the model was demonstrated to be successful in the identification of all directional (causal) interactions between the three different time scales described in the model. Thus, the methodology was prepared for the identification of causal cross-frequency phase–phase and phase–amplitude interactions in experimental and clinical neural data.
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Affiliation(s)
| | - Milan Paluš
- Correspondence: ; Tel.: +420-266-053-430; Fax: +420-286-585-789
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Pérez-Cervera A, Hlinka J. Perturbations both trigger and delay seizures due to generic properties of slow-fast relaxation oscillators. PLoS Comput Biol 2021; 17:e1008521. [PMID: 33780437 PMCID: PMC8032201 DOI: 10.1371/journal.pcbi.1008521] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 04/08/2021] [Accepted: 02/22/2021] [Indexed: 01/24/2023] Open
Abstract
The mechanisms underlying the emergence of seizures are one of the most important unresolved issues in epilepsy research. In this paper, we study how perturbations, exogenous or endogenous, may promote or delay seizure emergence. To this aim, due to the increasingly adopted view of epileptic dynamics in terms of slow-fast systems, we perform a theoretical analysis of the phase response of a generic relaxation oscillator. As relaxation oscillators are effectively bistable systems at the fast time scale, it is intuitive that perturbations of the non-seizing state with a suitable direction and amplitude may cause an immediate transition to seizure. By contrast, and perhaps less intuitively, smaller amplitude perturbations have been found to delay the spontaneous seizure initiation. By studying the isochrons of relaxation oscillators, we show that this is a generic phenomenon, with the size of such delay depending on the slow flow component. Therefore, depending on perturbation amplitudes, frequency and timing, a train of perturbations causes an occurrence increase, decrease or complete suppression of seizures. This dependence lends itself to analysis and mechanistic understanding through methods outlined in this paper. We illustrate this methodology by computing the isochrons, phase response curves and the response to perturbations in several epileptic models possessing different slow vector fields. While our theoretical results are applicable to any planar relaxation oscillator, in the motivating context of epilepsy they elucidate mechanisms of triggering and abating seizures, thus suggesting stimulation strategies with effects ranging from mere delaying to full suppression of seizures. Despite its simplicity, the modelling of epileptic dynamics as a slow-fast transition between low and high activity states mediated by some slow feedback variable is a relatively novel albeit fruitful approach. This study is the first, to our knowledge, characterizing the response of such slow-fast models of epileptic brain to perturbations by computing its isochrons. Besides its numerical computation, we theoretically determine which factors shape the geometry of isochrons for planar slow-fast oscillators. As a consequence, we introduce a theoretical approach providing a clear understanding of the response of perturbations of slow-fast oscillators. Within the epilepsy context, this elucidates the origin of the contradictory role of interictal epileptiform discharges in the transition to seizure, manifested by both pro-convulsive and anti-convulsive effect depending on the amplitude, frequency and timing. More generally, this paper provides theoretical framework highlighting the role of the slow flow component on the response to perturbations in relaxation oscillators, pointing to the general phenomena in such slow-fast oscillators ubiquitous in biological systems.
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Affiliation(s)
- Alberto Pérez-Cervera
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- * E-mail: (AP); (JH)
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
- * E-mail: (AP); (JH)
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Kudlacek J, Chvojka J, Kumpost V, Hermanovska B, Posusta A, Jefferys JGR, Maturana MI, Novak O, Cook MJ, Otahal J, Hlinka J, Jiruska P. Long-term seizure dynamics are determined by the nature of seizures and the mutual interactions between them. Neurobiol Dis 2021; 154:105347. [PMID: 33771663 DOI: 10.1016/j.nbd.2021.105347] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/05/2021] [Accepted: 03/22/2021] [Indexed: 02/07/2023] Open
Abstract
The seemingly random and unpredictable nature of seizures is a major debilitating factor for people with epilepsy. An increasing body of evidence demonstrates that the epileptic brain exhibits long-term fluctuations in seizure susceptibility, and seizure emergence seems to be a consequence of processes operating over multiple temporal scales. A deeper insight into the mechanisms responsible for long-term seizure fluctuations may provide important information for understanding the complex nature of seizure genesis. In this study, we explored the long-term dynamics of seizures in the tetanus toxin model of temporal lobe epilepsy. The results demonstrate the existence of long-term fluctuations in seizure probability, where seizures form clusters in time and are then followed by seizure-free periods. Within each cluster, seizure distribution is non-Poissonian, as demonstrated by the progressively increasing inter-seizure interval (ISI), which marks the approaching cluster termination. The lengthening of ISIs is paralleled by: increasing behavioral seizure severity, the occurrence of convulsive seizures, recruitment of extra-hippocampal structures and the spread of electrographic epileptiform activity outside of the limbic system. The results suggest that repeated non-convulsive seizures obey the 'seizures-beget-seizures' principle, leading to the occurrence of convulsive seizures, which decrease the probability of a subsequent seizure and, thus, increase the following ISI. The cumulative effect of repeated convulsive seizures leads to cluster termination, followed by a long inter-cluster period. We propose that seizures themselves are an endogenous factor that contributes to long-term fluctuations in seizure susceptibility and their mutual interaction determines the future evolution of disease activity.
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Affiliation(s)
- Jan Kudlacek
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Vojtech Kumpost
- Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Barbora Hermanovska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Antonin Posusta
- Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - John G R Jefferys
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Matias I Maturana
- The Graeme Clark Institute & Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia; Seer Medical, Melbourne, Australia
| | - Ondrej Novak
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Mark J Cook
- The Graeme Clark Institute & Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Jakub Otahal
- Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Nonlinear Modelling, Institute of Computer Science of the Czech Academy of Sciences, Prague 182 07, Czech Republic; National Institute of Mental Health, Klecany, Czech Republic.
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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Meisenhelter S, Quon RJ, Steimel SA, Testorf ME, Camp EJ, Moein P, Culler GW, Gross RE, Lega BC, Sperling MR, Kahana MJ, Jobst BC. Interictal Epileptiform Discharges are Task Dependent and are Associated with Lasting Electrocorticographic Changes. Cereb Cortex Commun 2021; 2:tgab019. [PMID: 34296164 PMCID: PMC8152941 DOI: 10.1093/texcom/tgab019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 12/24/2022] Open
Abstract
The factors that control the occurrence of interictal epileptiform discharges (IEDs) are not well understood. We suspected that this phenomenon reflects an attention-dependent suppression of interictal epileptiform activity. We hypothesized that IEDs would occur less frequently when a subject viewed a task-relevant stimulus compared with viewing a blank screen. Furthermore, IEDs have been shown to impair memory when they occur in certain regions during the encoding or recall phases of a memory task. Although these discharges have a short duration, their impact on memory suggests that they have longer lasting electrophysiological effects. We found that IEDs were associated with an increase in low-frequency power and a change in the balance between low- and high-frequency oscillations for several seconds. We found that the occurrence of IEDs is modified by whether a subject is attending to a word displayed on screen or is observing a blank screen. In addition, we found that discharges in brain regions in every lobe impair memory. These findings elucidate the relationship between IEDs and memory impairment and reveal the task dependence of the occurrence of IEDs.
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Affiliation(s)
- Stephen Meisenhelter
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766, USA
- Department of Neurology, Geisel School of Medicine at Dartmouth College Hanover, NH 03755, United States
| | - Robert J Quon
- Department of Neurology, Geisel School of Medicine at Dartmouth College Hanover, NH 03755, United States
| | - Sarah A Steimel
- Department of Neurology, Geisel School of Medicine at Dartmouth College Hanover, NH 03755, United States
| | - Markus E Testorf
- Thayer School of Engineering at Dartmouth College, Hanover, NH 03755, United States
| | - Edward J Camp
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766, USA
| | - Payam Moein
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766, USA
| | - George W Culler
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University, Atlanta, GA 30322, United States
| | - Bradley C Lega
- Department of Neurosurgery, University of Texas-Southwestern, Dallas, TX 75390, United States
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19144, United States
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Barbara C Jobst
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766, USA
- Department of Neurology, Geisel School of Medicine at Dartmouth College Hanover, NH 03755, United States
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Hagemann A, Wilting J, Samimizad B, Mormann F, Priesemann V. Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex. PLoS Comput Biol 2021; 17:e1008773. [PMID: 33684101 PMCID: PMC7971851 DOI: 10.1371/journal.pcbi.1008773] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 03/18/2021] [Accepted: 02/04/2021] [Indexed: 11/18/2022] Open
Abstract
Epileptic seizures are characterized by abnormal and excessive neural activity, where cortical network dynamics seem to become unstable. However, most of the time, during seizure-free periods, cortex of epilepsy patients shows perfectly stable dynamics. This raises the question of how recurring instability can arise in the light of this stable default state. In this work, we examine two potential scenarios of seizure generation: (i) epileptic cortical areas might generally operate closer to instability, which would make epilepsy patients generally more susceptible to seizures, or (ii) epileptic cortical areas might drift systematically towards instability before seizure onset. We analyzed single-unit spike recordings from both the epileptogenic (focal) and the nonfocal cortical hemispheres of 20 epilepsy patients. We quantified the distance to instability in the framework of criticality, using a novel estimator, which enables an unbiased inference from a small set of recorded neurons. Surprisingly, we found no evidence for either scenario: Neither did focal areas generally operate closer to instability, nor were seizures preceded by a drift towards instability. In fact, our results from both pre-seizure and seizure-free intervals suggest that despite epilepsy, human cortex operates in the stable, slightly subcritical regime, just like cortex of other healthy mammalians.
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Affiliation(s)
- Annika Hagemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Jens Wilting
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Bita Samimizad
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Florian Mormann
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Viola Priesemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Bernstein Center for Computational Neuroscience (BCCN) Göttingen, Germany
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Karpov OE, Grubov VV, Maksimenko VA, Utaschev N, Semerikov VE, Andrikov DA, Hramov AE. Noise amplification precedes extreme epileptic events on human EEG. Phys Rev E 2021; 103:022310. [PMID: 33735967 DOI: 10.1103/physreve.103.022310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 01/19/2021] [Indexed: 06/12/2023]
Abstract
Extreme events are rare and sudden abnormal deviations of the system's behavior from a typical state. Statistical analysis reveals that if the time series contains extreme events, its distribution has a heavy tail. In dynamical systems, extreme events often occur due to developing instability preceded by noise amplification. Here, we apply this theory to analyze generalized epileptic seizures in the human brain. First, we demonstrate that the time series of electroencephalogram (EEG) spectral power in a frequency band of 1-5 Hz obeys a heavy-tailed distribution, confirming the presence of extreme events. Second, we report that noise on EEG signals gradually increases before the seizure onset. Thus, we hypothesize that generalized epileptic seizures in humans are the extreme events emerging from instability accompanied by preictal noise amplification similar to other dynamical systems.
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Affiliation(s)
- Oleg E Karpov
- National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia
| | - Vadim V Grubov
- Research and Production Company "Immersmed", Moscow 105203, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia
| | - Vladimir A Maksimenko
- Research and Production Company "Immersmed", Moscow 105203, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia
| | - Nikita Utaschev
- National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia
| | | | - Denis A Andrikov
- Research and Production Company "Immersmed", Moscow 105203, Russia
| | - Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia
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Abstract
PURPOSE OF REVIEW Epilepsy is a dynamical disorder of the brain characterized by sudden, seemingly unpredictable transitions to the ictal state. When and how these transitions occur remain unresolved questions in neurology. RECENT FINDINGS Modelling work based on dynamical systems theory proposed that a slow control parameter is necessary to explain the transition between interictal and ictal states. Recently, converging evidence from chronic EEG datasets unravelled the existence of cycles of epileptic brain activity at multiple timescales - circadian, multidien (over multiple days) and circannual - which could reflect cyclical changes in a slow control parameter. This temporal structure of epilepsy has theoretical implications and argues against the conception of seizures as completely random events. The practical significance of cycles in epilepsy is highlighted by their predictive value in computational models for seizure forecasting. SUMMARY The canonical randomness of seizures is being reconsidered in light of cycles of brain activity discovered through chronic EEG. This paradigm shift motivates development of next-generation devices to track more closely fluctuations in epileptic brain activity that determine time-varying seizure risk.
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Carvalho VR, Moraes MFD, Cash SS, Mendes EMAM. Active probing to highlight approaching transitions to ictal states in coupled neural mass models. PLoS Comput Biol 2021; 17:e1008377. [PMID: 33493165 PMCID: PMC7861539 DOI: 10.1371/journal.pcbi.1008377] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 02/04/2021] [Accepted: 12/02/2020] [Indexed: 01/07/2023] Open
Abstract
The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using two coupled populations of the well-studied Wendling neural mass model. Different model settings are evaluated, shifting parameters (excitability, slow inhibition, or inter-population coupling gains) from normal towards ictal states while probing stimuli are applied every 2 seconds to the input of either one or both populations. The correlation between the extracted features and the ictogenic parameter shifting indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were mostly restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems' resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions.
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Affiliation(s)
- Vinícius Rezende Carvalho
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Márcio Flávio Dutra Moraes
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Eduardo Mazoni Andrade Marçal Mendes
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Paschen E, Elgueta C, Heining K, Vieira DM, Kleis P, Orcinha C, Häussler U, Bartos M, Egert U, Janz P, Haas CA. Hippocampal low-frequency stimulation prevents seizure generation in a mouse model of mesial temporal lobe epilepsy. eLife 2020; 9:54518. [PMID: 33349333 PMCID: PMC7800381 DOI: 10.7554/elife.54518] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 12/13/2020] [Indexed: 12/18/2022] Open
Abstract
Mesial temporal lobe epilepsy (MTLE) is the most common form of focal, pharmacoresistant epilepsy in adults and is often associated with hippocampal sclerosis. Here, we established the efficacy of optogenetic and electrical low-frequency stimulation (LFS) in interfering with seizure generation in a mouse model of MTLE. Specifically, we applied LFS in the sclerotic hippocampus to study the effects on spontaneous subclinical and evoked generalized seizures. We found that stimulation at 1 Hz for 1 hr resulted in an almost complete suppression of spontaneous seizures in both hippocampi. This seizure-suppressive action during daily stimulation remained stable over several weeks. Furthermore, LFS for 30 min before a pro-convulsive stimulus successfully prevented seizure generalization. Finally, acute slice experiments revealed a reduced efficacy of perforant path transmission onto granule cells upon LFS. Taken together, our results suggest that hippocampal LFS constitutes a promising approach for seizure control in MTLE.
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Affiliation(s)
- Enya Paschen
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Claudio Elgueta
- Systemic and Cellular Neurophysiology, Institute for Physiology I, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Katharina Heining
- Biomicrotechnology, Department of Microsystems Engineering - IMTEK, Faculty of Engineering, University of Freiburg, Freiburg, Germany.,Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
| | - Diego M Vieira
- Biomicrotechnology, Department of Microsystems Engineering - IMTEK, Faculty of Engineering, University of Freiburg, Freiburg, Germany.,Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
| | - Piret Kleis
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Catarina Orcinha
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Ute Häussler
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany.,Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marlene Bartos
- Systemic and Cellular Neurophysiology, Institute for Physiology I, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ulrich Egert
- Biomicrotechnology, Department of Microsystems Engineering - IMTEK, Faculty of Engineering, University of Freiburg, Freiburg, Germany.,Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
| | - Philipp Janz
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Carola A Haas
- Experimental Epilepsy Research, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany.,Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Mihály I, Orbán-Kis K, Gáll Z, Berki ÁJ, Bod RB, Szilágyi T. Amygdala Low-Frequency Stimulation Reduces Pathological Phase-Amplitude Coupling in the Pilocarpine Model of Epilepsy. Brain Sci 2020; 10:brainsci10110856. [PMID: 33202818 PMCID: PMC7696538 DOI: 10.3390/brainsci10110856] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 10/31/2020] [Accepted: 11/11/2020] [Indexed: 02/07/2023] Open
Abstract
Temporal-lobe epilepsy (TLE) is the most common type of drug-resistant epilepsy and warrants the development of new therapies, such as deep-brain stimulation (DBS). DBS was applied to different brain regions for patients with epilepsy; however, the mechanisms of action are not fully understood. Therefore, we tried to characterize the effect of amygdala DBS on hippocampal electrical activity in the lithium-pilocarpine model in male Wistar rats. After status epilepticus (SE) induction, seizure patterns were determined based on continuous video recordings. Recording electrodes were inserted in the left and right hippocampus and a stimulating electrode in the left basolateral amygdala of both Pilo and age-matched control rats 10 weeks after SE. Daily stimulation protocol consisted of 4 × 50 s stimulation trains (4-Hz, regular interpulse interval) for 10 days. The hippocampal electroencephalogram was analyzed offline: interictal epileptiform discharge (IED) frequency, spectral analysis, and phase-amplitude coupling (PAC) between delta band and higher frequencies were measured. We found that the seizure rate and duration decreased (by 23% and 26.5%) and the decrease in seizure rate correlated negatively with the IED frequency. PAC was elevated in epileptic animals and DBS reduced the pathologically increased PAC and increased the average theta power (25.9% ± 1.1 vs. 30.3% ± 1.1; p < 0.01). Increasing theta power and reducing the PAC could be two possible mechanisms by which DBS may exhibit its antiepileptic effect in TLE; moreover, they could be used to monitor effectiveness of stimulation.
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Affiliation(s)
- István Mihály
- Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania; (K.O.-K.); (Á.-J.B.); (R.-B.B.), (T.S.)
- Correspondence: ; Tel.: +40-749-768-257
| | - Károly Orbán-Kis
- Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania; (K.O.-K.); (Á.-J.B.); (R.-B.B.), (T.S.)
| | - Zsolt Gáll
- Department of Pharmacology and Clinical Pharmacy, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania;
| | - Ádám-József Berki
- Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania; (K.O.-K.); (Á.-J.B.); (R.-B.B.), (T.S.)
| | - Réka-Barbara Bod
- Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania; (K.O.-K.); (Á.-J.B.); (R.-B.B.), (T.S.)
| | - Tibor Szilágyi
- Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania; (K.O.-K.); (Á.-J.B.); (R.-B.B.), (T.S.)
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Bernard C. Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy (MORE). Epilepsia 2020; 62 Suppl 1:S49-S68. [PMID: 33063860 DOI: 10.1111/epi.16716] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/15/2020] [Accepted: 09/15/2020] [Indexed: 12/26/2022]
Abstract
The occurrence of seizures at specific times of the day has been consistently observed for centuries in individuals with epilepsy. Electrophysiological recordings provide evidence that seizures have a higher probability of occurring at a given time during the night and day cycle in individuals with epilepsy here referred to as the seizure rush hour. Which mechanisms underlie such circadian rhythmicity of seizures? Why don't they occur every day at the same time? Which mechanisms may underlie their occurrence outside the rush hour? In this commentary, I present a hypothesis: MORE - Molecular Oscillations and Rhythmicity of Epilepsy, a conceptual framework to study and understand the mechanisms underlying the circadian rhythmicity of seizures and their probabilistic nature. The core of the hypothesis is the existence of ~24-hour oscillations of gene and protein expression throughout the body in different cells and organs. The orchestrated molecular oscillations control the rhythmicity of numerous body events, such as feeding and sleep. The concept developed here is that molecular oscillations may favor seizure genesis at preferred times, generating the condition for a seizure rush hour. However, the condition is not sufficient, as other factors are necessary for a seizure to occur. Studying these molecular oscillations may help us understand seizure genesis mechanisms and find new therapeutic targets and predictive biomarkers. The MORE hypothesis can be generalized to comorbidities and the slower multidien (week/month period) rhythmicity of seizures, a phenomenon addressed in another article in this issue of Epilepsia.
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Affiliation(s)
- Christophe Bernard
- Inserm, INS, Institut de Neurosciences des Systèmes, Aix Marseille Univ, Marseille, France
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43
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Ehrens D, Li A, Aeed F, Schiller Y, Sarma SV. Network Fragility for Seizure Genesis in an Acute in vivo Model of Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3695-3698. [PMID: 33018803 DOI: 10.1109/embc44109.2020.9175959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Epilepsy affects over 50 million people worldwide and 30% of patients' seizures are medically refractory. The process of localizing and removing the epileptogenic zone is error-prone and ill-posed in part because we do not understand how epilepsy manifests. It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. If the fragility of the cortical network could be computed over a period in which seizure genesis occurs, then it might elucidate network mechanisms correlated to the epileptogenic zone. In this study, we used local field potentials (LFP) from neocortex by implementing an acute model of epilepsy in mice. These recordings were used to develop a dynamical network model that quantifies the fragility of the nodes from LFP epochs of baseline activity, preictal and ictal states. Fragility was quantified by the generation of a linear time-varying model to which we then applied a perturbation to determine the sensitivity of nodes in the network. Spatiotemporal fragility maps showed clear quantifiable changes in the epileptogenic network's properties throughout different states of seizure genesis. We quantified this difference over a baseline, preictal and ictal periods to show that network fragility is modulated in the manifestation of epilepsy.
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Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions. Sci Rep 2020; 10:15408. [PMID: 32958802 PMCID: PMC7506027 DOI: 10.1038/s41598-020-72335-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/26/2020] [Indexed: 12/29/2022] Open
Abstract
An improved understanding of the mechanisms underlying neuromodulatory approaches to mitigate seizure onset is needed to identify clinical targets for the treatment of epilepsy. Using a Wilson–Cowan-motivated network of inhibitory and excitatory populations, we examined the role played by intrinsic and extrinsic stimuli on the network’s predisposition to sudden transitions into oscillatory dynamics, similar to the transition to the seizure state. Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such oscillations. Based on a combination of numerical simulations and mean-field analyses, our results suggest that high variance and/or high frequency stimulation waveforms can prevent multi-stability, a mathematical harbinger of sudden changes in network dynamics. By tuning the neurons’ responses to input, stimuli stabilize network dynamics away from these transitions. Furthermore, our research shows that such stabilization of neural activity occurs through a selective recruitment of inhibitory cells, providing a theoretical undergird for the known key role these cells play in both the healthy and diseased brain. Taken together, these findings provide new vistas on neuromodulatory approaches to stabilize neural microcircuit activity.
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Stirling RE, Cook MJ, Grayden DB, Karoly PJ. Seizure forecasting and cyclic control of seizures. Epilepsia 2020; 62 Suppl 1:S2-S14. [PMID: 32712968 DOI: 10.1111/epi.16541] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 02/02/2023]
Abstract
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Mark J Cook
- Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia.,Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
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46
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Saggio ML, Crisp D, Scott JM, Karoly P, Kuhlmann L, Nakatani M, Murai T, Dümpelmann M, Schulze-Bonhage A, Ikeda A, Cook M, Gliske SV, Lin J, Bernard C, Jirsa V, Stacey WC. A taxonomy of seizure dynamotypes. eLife 2020; 9:55632. [PMID: 32691734 PMCID: PMC7375810 DOI: 10.7554/elife.55632] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties. Epileptic seizures have been recognized for centuries. But it was only in the 1930s that it was realized that seizures are the result of out-of-control electrical activity in the brain. By placing electrodes on the scalp, doctors can identify when and where in the brain a seizure begins. But they cannot tell much about how the seizure behaves, that is, how it starts, stops or spreads to other areas. This makes it difficult to control and prevent seizures. It also helps explain why almost a third of patients with epilepsy continue to have seizures despite being on medication. Saggio, Crisp et al. have now approached this problem from a new angle using methods adapted from physics and engineering. In these fields, “dynamics research” has been used with great success to predict and control the behavior of complex systems like electrical power grids. Saggio, Crisp et al. reasoned that applying the same approach to the brain would reveal the dynamics of seizures and that such information could then be used to categorize seizures into groups with similar properties. This would in effect create for seizures what the periodic table is for the elements. Applying the dynamics research method to seizure data from more than a hundred patients from across the world revealed 16 types of seizure dynamics. These “dynamotypes” had distinct characteristics. Some were more common than others, and some tended to occur together. Individual patients showed different dynamotypes over time. By constructing a way to classify seizures based on the relationships between the dynamotypes, Saggio, Crisp et al. provide a new tool for clinicians and researchers studying epilepsy. Previous clinical tools have focused on the physical symptoms of a seizure (referred to as the phenotype) or its potential genetic causes (genotype). The current approach complements these tools by adding the dynamotype: how seizures start, spread and stop in the brain. This approach has the potential to lead to new branches of research and better understanding and treatment of seizures.
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Affiliation(s)
- Maria Luisa Saggio
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Dakota Crisp
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Jared M Scott
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Philippa Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Levin Kuhlmann
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Faculty of Information Technology, Monash University, Clayton, Australia
| | - Mitsuyoshi Nakatani
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Tomohiko Murai
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Mark Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Stephen V Gliske
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Jack Lin
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Christophe Bernard
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - William C Stacey
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States.,Department of Neurology, University of Michigan, Ann Arbor, United States
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47
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Liu Z, Luan G, Yang C, Guan Y, Liu C, Wang J, Wang M, Wang Q. Distinguishing Dependent-Stage Secondary Epileptogenesis in a Complex Case of Giant Hypothalamic Hamartoma With Assistance of a Computational Method. Front Neurol 2020; 11:478. [PMID: 32587568 PMCID: PMC7297952 DOI: 10.3389/fneur.2020.00478] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 05/01/2020] [Indexed: 11/18/2022] Open
Abstract
Besides gelastic seizures, hypothalamic hamartoma (HH) is also noted for its susceptibility to remote secondary epileptogenesis. Although clinical observations have demonstrated its existence, and a three-stage theory has been proposed, how to determine whether a remote symptom is spontaneous or dependent on epileptic activities of HH is difficult in some cases. Herein, we report a case of new non-gelastic seizures in a 9-year-old female associated with a postoperatively remaining HH. Electrophysiological examinations and stereo-electroencephalography (SEEG) demonstrated seizure onsets with slow-wave and fast activities on the outside of the HH. By using computational methodologies to calculate the network dynamic effective connectivities, the importance of HH in the epileptic network was revealed. After SEEG-guided thermal coagulation of the remaining HH, the patient finally was seizure-free at the 2-year follow-up. This case showed the ability of computational methods to reveal information underlying complex SEEG signals, and further demonstrated the dependent-stage secondary epileptogenesis, which has been rarely reported.
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Affiliation(s)
- Zhao Liu
- Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Epilepsy, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Guoming Luan
- Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Epilepsy, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Beijing, China
| | - Chuanzuo Yang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Yuguang Guan
- Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Changqing Liu
- Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Jing Wang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Mengyang Wang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
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48
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Maturana MI, Meisel C, Dell K, Karoly PJ, D'Souza W, Grayden DB, Burkitt AN, Jiruska P, Kudlacek J, Hlinka J, Cook MJ, Kuhlmann L, Freestone DR. Critical slowing down as a biomarker for seizure susceptibility. Nat Commun 2020; 11:2172. [PMID: 32358560 PMCID: PMC7195436 DOI: 10.1038/s41467-020-15908-3] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 03/30/2020] [Indexed: 02/04/2023] Open
Abstract
The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms. Critical slowing (associated with increased variance and autocorrelation) can precede critical state transitions. Here, the authors show critical slowing can be used as a marker in seizure forecasting algorithms.
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Affiliation(s)
- Matias I Maturana
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia. .,Seer Medical, Melbourne, Australia.
| | - Christian Meisel
- Department of Neurology, University Clinic Carl Gustav Carus, Dresden, Germany.,Boston Children's Hospital, Boston, MA, USA
| | - Katrina Dell
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Wendyl D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.,Department of Developmental Epileptology, Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic
| | - Jan Kudlacek
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.,Department of Developmental Epileptology, Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic.,Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Levin Kuhlmann
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Faculty of Information Technology, Monash University, Clayton, Victoria, Australia.,Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Victoria, Australia
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49
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Lan BL, Liew YW, Toda M, Kamsani SH. Flickering of cardiac state before the onset and termination of atrial fibrillation. CHAOS (WOODBURY, N.Y.) 2020; 30:053137. [PMID: 32491883 DOI: 10.1063/1.5130524] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 04/22/2020] [Indexed: 06/11/2023]
Abstract
Complex dynamical systems can shift abruptly from a stable state to an alternative stable state at a tipping point. Before the critical transition, the system either slows down in its recovery rate or flickers between the basins of attraction of the alternative stable states. Whether the heart critically slows down or flickers before it transitions into and out of paroxysmal atrial fibrillation (PAF) is still an open question. To address this issue, we propose a novel definition of cardiac states based on beat-to-beat (RR) interval fluctuations derived from electrocardiogram data. Our results show the cardiac state flickers before PAF onset and termination. Prior to onset, flickering is due to a "tug-of-war" between the sinus node (the natural pacemaker) and atrial ectopic focus/foci (abnormal pacemakers), or the pacing by the latter interspersed among the pacing by the former. It may also be due to an abnormal autonomic modulation of the sinus node. This abnormal modulation may be the sole cause of flickering prior to termination since atrial ectopic beats are absent. Flickering of the cardiac state could potentially be used as part of an early warning or screening system for PAF and guide the development of new methods to prevent or terminate PAF. The method we have developed to define system states and use them to detect flickering can be adapted to study critical transition in other complex systems.
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Affiliation(s)
- Boon Leong Lan
- Electrical and Computer Systems Engineering & Advanced Engineering Platform, School of Engineering, Monash University, 47500 Bandar Sunway, Malaysia
| | - Yew Wai Liew
- Electrical and Computer Systems Engineering & Advanced Engineering Platform, School of Engineering, Monash University, 47500 Bandar Sunway, Malaysia
| | - Mikito Toda
- Laboratory of Non-equilibrium Dynamics, Research Group of Physics, Faculty Division of Natural Sciences, Nara Women's University, Nara 630-8506, Japan
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50
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Antiepileptic drugs induce subcritical dynamics in human cortical networks. Proc Natl Acad Sci U S A 2020; 117:11118-11125. [PMID: 32358198 DOI: 10.1073/pnas.1911461117] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Cortical network functioning critically depends on finely tuned interactions to afford neuronal activity propagation over long distances while avoiding runaway excitation. This importance is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability in psychiatric and neurological diseases, such as epilepsy. Theory and experiment suggest that the control of activity propagation by network interactions can be adequately described by a branching process. This hypothesis is partially supported by strong evidence for balanced spatiotemporal dynamics observed in the cerebral cortex; however, evidence of a causal relationship between network interactions and cortex activity, as predicted by a branching process, is missing in humans. Here this cause-effect relationship is tested by monitoring cortex activity under systematic pharmacological reduction of cortical network interactions with antiepileptic drugs. This study reports that cortical activity cascades, presented by the propagating patterns of epileptic spikes, as well as temporal correlations decline precisely as predicted for a branching process. The results provide a missing link to the branching process theory of cortical network function with implications for understanding the foundations of cortical excitability and its monitoring in conditions like epilepsy.
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