1
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Cancino-Fuentes N, Manasanch A, Covelo J, Suarez-Perez A, Fernandez E, Matsoukis S, Guger C, Illa X, Guimerà-Brunet A, Sanchez-Vives MV. Recording physiological and pathological cortical activity and exogenous electric fields using graphene microtransistor arrays in vitro. NANOSCALE 2024; 16:664-677. [PMID: 38100059 DOI: 10.1039/d3nr03842d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
Graphene-based solution-gated field-effect transistors (gSGFETs) allow the quantification of the brain's full-band signal. Extracellular alternating current (AC) signals include local field potentials (LFP, population activity within a reach of hundreds of micrometers), multiunit activity (MUA), and ultimately single units. Direct current (DC) potentials are slow brain signals with a frequency under 0.1 Hz, and commonly filtered out by conventional AC amplifiers. This component conveys information about what has been referred to as "infraslow" activity. We used gSGFET arrays to record full-band patterns from both physiological and pathological activity generated by the cerebral cortex. To this end, we used an in vitro preparation of cerebral cortex that generates spontaneous rhythmic activity, such as that occurring in slow wave sleep. This examination extended to experimentally induced pathological activities, including epileptiform discharges and cortical spreading depression. Validation of recordings obtained via gSGFETs, including both AC and DC components, was accomplished by cross-referencing with well-established technologies, thereby quantifying these components across different activity patterns. We then explored an additional gSGFET potential application, which is the measure of externally induced electric fields such as those used in therapeutic neuromodulation in humans. Finally, we tested the gSGFETs in human cortical slices obtained intrasurgically. In conclusion, this study offers a comprehensive characterization of gSGFETs for brain recordings, with a focus on potential clinical applications of this emerging technology.
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Affiliation(s)
| | - Arnau Manasanch
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - Joana Covelo
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - Alex Suarez-Perez
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | | | - Stratis Matsoukis
- g.tec medical engineering, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Xavi Illa
- Instituto de Microelectrónica de Barcelona (IMB-CNM, CSIC), Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain
| | - Anton Guimerà-Brunet
- Instituto de Microelectrónica de Barcelona (IMB-CNM, CSIC), Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain
| | - Maria V Sanchez-Vives
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
- ICREA, Barcelona, Spain
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2
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Zhang H, Yan L, Peng X, Jiang L, Zhang J, Chen J, Hu Y. The prospective study of 54 children with electrical status epilepticus during sleep: How to simplify the electroencephalogram diagnosis and guide the treatment. Epileptic Disord 2023; 25:690-701. [PMID: 37408096 DOI: 10.1002/epd2.20095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 06/11/2023] [Accepted: 06/28/2023] [Indexed: 07/07/2023]
Abstract
OBJECTIVE To simplify the electroencephalogram (EEG) diagnosis and guide the treatment of electrical status epilepticus during sleep (ESES). METHODS We recruited 54 children with ESES from December 2019 to December 2020 and compared various spike-wave index (SWI) calculation methods. Time-frequency analysis assessed the correlation between high-frequency oscillations energy and the SWI. We divided 42 children into responder and non-responder treatment groups based on the observations made during a 12-month follow-up period and evaluate different treatment and the independent risk factors of refractory ESES. RESULTS The SWI of 5 min before the second sleep cycle of non-rapid eye movement (NREM; long method II) and that of all NREM sleep (total method) were not significantly different (p = .06). The average energy of γ (r = .288, p = .002) and ripple (r = .203, p = .04) oscillations were correlated with the SWI. Multivariable logistic regression analysis showed that encephalomalacia was an independent risk factor for refractory ESES (OR: 10.48, 95% CI: 1.62-67.63). The clinical seizure improvement rates of anti-seizure medications (ASMs), ASMs with benzodiazepines, and ASMs with benzodiazepines and steroids after 12 months were 9.3%, 42.8%, and 53.8%, EEG improvement rate were 5.5%, 30.9% and 37%, respectively. The intelligence of the children in the responder treatment group has improved during the 1-year follow-up. SIGNIFICANCE These findings demonstrate EEG and clinical features of ESES and may provide basis for simplifying diagnosis and guiding the treatment of children with ESES.
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Affiliation(s)
- Han Zhang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Lisi Yan
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xiaoling Peng
- Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
| | - Li Jiang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Junjiao Zhang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Jin Chen
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
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3
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Zhao X, Peng X, Niu K, Li H, He L, Yang F, Wu T, Chen D, Zhang Q, Ouyang M, Guo J, Pan Y. A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy. Front Neuroinform 2022; 16:771965. [PMID: 36156983 PMCID: PMC9500293 DOI: 10.3389/fninf.2022.771965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 08/03/2022] [Indexed: 12/03/2022] Open
Abstract
Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.
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Affiliation(s)
- Xiangyu Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xueping Peng
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
- *Correspondence: Xueping Peng
| | - Ke Niu
- Computer School, Beijing Information Science and Technology University, Beijing, China
| | - Hailong Li
- Department of Radiology, Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Lili He
- Department of Radiology, Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Feng Yang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ting Wu
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Magnetoencephalography, Nanjing Brain Hospital, Affiliated to Nanjing Medical University, Nanjing, China
- Ting Wu
| | - Duo Chen
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
| | - Qiusi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Menglin Ouyang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China
| | - Jiayang Guo
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- Department of Hematology, School of Medicine, Xiamen University, Xiamen, China
- Jiayang Guo
| | - Yijie Pan
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Ningbo Institute of Information Technology Application, Chinese Academy of Sciences, Ningbo, China
- Yijie Pan
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4
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Lee S, Henry J, Tryba AK, Esengul Y, Warnke P, Wu S, van Drongelen W. Digital reconstruction of infraslow activity in human intracranial ictal recordings using a deconvolution-based inverse filter. Sci Rep 2022; 12:13701. [PMID: 35953580 PMCID: PMC9372169 DOI: 10.1038/s41598-022-18071-5] [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: 06/23/2022] [Accepted: 08/04/2022] [Indexed: 11/30/2022] Open
Abstract
Infraslow activity (ISA) is a biomarker that has recently become of interest in the characterization of seizure recordings. Recent data from a small number of studies have suggested that the epileptogenic zone may be identified by the presence of ISA. Investigation of low frequency activity in clinical seizure recordings, however, has been hampered by technical limitations. EEG systems necessarily include a high-pass filter early in the measurement chain to remove large artifactual drifts that can saturate recording elements such as the amplifier. This filter, unfortunately, attenuates legitimately seizure-related low frequencies, making ISA difficult to study in clinical EEG recordings. In this study, we present a deconvolution-based digital inverse filter that allows recovery of attenuated low frequency activity in intracranial recordings of temporal lobe epilepsy patients. First, we show that the unit impulse response (UIR) of an EEG system can be characterized by differentiation of the system's step response. As proof of method, we present several examples that show that the low frequency component of a high-pass filtered signal can be restored by deconvolution with the UIR. We then demonstrate that this method can be applied to biologically relevant signals including clinical EEG recordings obtained from seizure patients. Finally, we discuss how this method can be applied to study ISA to identify and assess the seizure onset zone.
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Affiliation(s)
- Somin Lee
- Medical Scientist Training Program, The University of Chicago, Chicago, IL, 60637, USA
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60637, USA
| | - Julia Henry
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60637, USA
| | - Andrew K Tryba
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60637, USA
| | - Yasar Esengul
- Department of Neurology, The University of Chicago, Chicago, IL, 60637, USA
| | - Peter Warnke
- Department of Neurosurgery, The University of Chicago, Chicago, IL, 60637, USA
| | - Shasha Wu
- Department of Neurology, The University of Chicago, Chicago, IL, 60637, USA
| | - Wim van Drongelen
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60637, USA.
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60637, USA.
- Committee On Computational Neuroscience, The University of Chicago, Chicago, IL, 60637, USA.
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5
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Li X, Zhang H, Lai H, Wang J, Wang W, Yang X. High-Frequency Oscillations and Epileptogenic Network. Curr Neuropharmacol 2022; 20:1687-1703. [PMID: 34503414 PMCID: PMC9881061 DOI: 10.2174/1570159x19666210908165641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/26/2021] [Accepted: 08/31/2021] [Indexed: 11/22/2022] Open
Abstract
Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.
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Affiliation(s)
- Xiaonan Li
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | | | | | - Jiaoyang Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Wei Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China,Address correspondence to this author at the Bioland Laboratory, Guangzhou, China; Tel: 86+ 18515855127; E-mail:
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6
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Bonaccini Calia A, Masvidal-Codina E, Smith TM, Schäfer N, Rathore D, Rodríguez-Lucas E, Illa X, De la Cruz JM, Del Corro E, Prats-Alfonso E, Viana D, Bousquet J, Hébert C, Martínez-Aguilar J, Sperling JR, Drummond M, Halder A, Dodd A, Barr K, Savage S, Fornell J, Sort J, Guger C, Villa R, Kostarelos K, Wykes RC, Guimerà-Brunet A, Garrido JA. Full-bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene microtransistor depth neural probes. NATURE NANOTECHNOLOGY 2022; 17:301-309. [PMID: 34937934 DOI: 10.1038/s41565-021-01041-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 11/02/2021] [Indexed: 06/14/2023]
Abstract
Mapping the entire frequency bandwidth of brain electrophysiological signals is of paramount importance for understanding physiological and pathological states. The ability to record simultaneously DC-shifts, infraslow oscillations (<0.1 Hz), typical local field potentials (0.1-80 Hz) and higher frequencies (80-600 Hz) using the same recording site would particularly benefit preclinical epilepsy research and could provide clinical biomarkers for improved seizure onset zone delineation. However, commonly used metal microelectrode technology suffers from instabilities that hamper the high fidelity of DC-coupled recordings, which are needed to access signals of very low frequency. In this study we used flexible graphene depth neural probes (gDNPs), consisting of a linear array of graphene microtransistors, to concurrently record DC-shifts and high-frequency neuronal activity in awake rodents. We show here that gDNPs can reliably record and map with high spatial resolution seizures, pre-ictal DC-shifts and seizure-associated spreading depolarizations together with higher frequencies through the cortical laminae to the hippocampus in a mouse model of chemically induced seizures. Moreover, we demonstrate the functionality of chronically implanted devices over 10 weeks by recording with high fidelity spontaneous spike-wave discharges and associated infraslow oscillations in a rat model of absence epilepsy. Altogether, our work highlights the suitability of this technology for in vivo electrophysiology research, and in particular epilepsy research, by allowing stable and chronic DC-coupled recordings.
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Affiliation(s)
- Andrea Bonaccini Calia
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Eduard Masvidal-Codina
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Trevor M Smith
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK
| | - Nathan Schäfer
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Daman Rathore
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK
| | - Elisa Rodríguez-Lucas
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Xavi Illa
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Jose M De la Cruz
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Elena Del Corro
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Elisabet Prats-Alfonso
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Damià Viana
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Jessica Bousquet
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Clement Hébert
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Javier Martínez-Aguilar
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Justin R Sperling
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
| | - Matthew Drummond
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Arnab Halder
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Abbie Dodd
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Katharine Barr
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Sinead Savage
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Jordina Fornell
- Departament de Fisica, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Jordi Sort
- Departament de Fisica, Universitat Autonoma de Barcelona, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
| | - Christoph Guger
- g.tec medical engineering, Guger Technologies, Schiedlberg, Austria
| | - Rosa Villa
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Kostas Kostarelos
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Rob C Wykes
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK.
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK.
| | - Anton Guimerà-Brunet
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, Spain.
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
| | - Jose A Garrido
- Catalan Institute of Nanoscience and Nanotechnology, CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain.
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.
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7
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Yan L, Li L, Chen J, Wang L, Jiang L, Hu Y. Application of High-Frequency Oscillations on Scalp EEG in Infant Spasm: A Prospective Controlled Study. Front Hum Neurosci 2021; 15:682011. [PMID: 34177501 PMCID: PMC8223253 DOI: 10.3389/fnhum.2021.682011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/17/2021] [Indexed: 01/22/2023] Open
Abstract
Objective We quantitatively analyzed high-frequency oscillations (HFOs) using scalp electroencephalography (EEG) in patients with infantile spasms (IS). Methods We enrolled 60 children with IS hospitalized from January 2019 to August 2020. Sixty healthy age-matched children comprised the control group. Time-frequency analysis was used to quantify γ, ripple, and fast ripple (FR) oscillation energy changes. Results γ, ripple, and FR oscillations dominated in the temporal and frontal lobes. The average HFO energy of the sleep stage is lower than that of the wake stage in the same frequency bands in both the normal control (NC) and IS groups (P < 0.05). The average HFO energy of the IS group was significantly higher than that of the NC group in γ band during sleep stage (P < 0.01). The average HFO energy of S and Post-S stage were higher than that of sleep stage in γ band (P < 0.05). In the ripple band, the average HFO energy of Pre-S, S, and Post-S stage was higher than that of sleep stage (P < 0.05). Before treatment, there was no significant difference in BASED score between the effective and ineffective groups. The interaction of curative efficacy × frequency and the interaction of curative efficacy × state are statistically significant. The average HFO energy of the effective group was lower than that of the ineffective group in the sleep stage (P < 0.05). For the 16 children deemed "effective" in the IS group, the average HFO energy of three frequency bands was not significantly different before compared with after treatment. Significance Scalp EEG can record HFOs. The energy of HFOs can distinguish physiological HFOs from pathological ones more accurately than frequency. On scalp EEG, γ oscillations can better detect susceptibility to epilepsy than ripple and FR oscillations. HFOs can trigger spasms. The analysis of average HFO energy can be used as a predictor of the effectiveness of epilepsy treatment.
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Affiliation(s)
- Lisi Yan
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Lin Li
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Jin Chen
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Wang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Jiang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
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8
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Guo J, Li H, Sun X, Qi L, Qiao H, Pan Y, Xiang J, Ji R. Detecting High Frequency Oscillations for Stereoelectroencephalography in Epilepsy via Hypergraph Learning. IEEE Trans Neural Syst Rehabil Eng 2021; 29:587-596. [PMID: 33534708 DOI: 10.1109/tnsre.2021.3056685] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Successful epilepsy surgeries depend highly on pre-operative localization of epileptogenic zones. Stereoelectroencephalography (SEEG) records interictal and ictal activities of the epilepsy in order to precisely find and localize epileptogenic zones in clinical practice. While it is difficult to find distinct ictal onset patterns generated the seizure onset zone from SEEG recordings in a confined region, high frequency oscillations are commonly considered as putative biomarkers for the identification of epileptogenic zones. Therefore, automatic and accurate detection of high frequency oscillations in SEEG signals is crucial for timely clinical evaluation. This work formulates the detection of high frequency oscillations as a signal segment classification problem and develops a hypergraph-based detector to automatically detect high frequency oscillations such that human experts can visually review SEEG signals. We evaluated our method on 4,000 signal segments from clinical SEEG recordings that contain both ictal and interictal data obtained from 19 patients who suffer from refractory focal epilepsy. The experimental results demonstrate the effectiveness of the proposed detector that can successfully localize interictal high frequency oscillations and outperforms multiple peer machine learning methods. In particular, the proposed detector achieved 90.7% in accuracy, 80.9% in sensitivity, and 96.9% in specificity.
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Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101720] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Lee S, Issa NP, Rose S, Tao JX, Warnke PC, Towle VL, van Drongelen W, Wu S. DC shifts, high frequency oscillations, ripples and fast ripples in relation to the seizure onset zone. Seizure 2019; 77:52-58. [PMID: 31101405 DOI: 10.1016/j.seizure.2019.05.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/22/2019] [Accepted: 05/02/2019] [Indexed: 10/26/2022] Open
Abstract
Efforts to improve epilepsy surgery outcomes have led to increased interest in the study of electroencephalographic oscillations outside the conventional EEG bands. These include fast activity above the gamma band, known as high frequency oscillations (HFOs), and infraslow activity (ISA) below the delta band, sometimes referred to as direct current (DC) or ictal baseline shifts (IBS). HFOs in particular have been extensively studied as potential biomarkers for epileptogenic tissue in light of evidence showing that resection of brain tissue containing HFOs is associated with good surgical outcomes. Not all HFOs are conclusively pathological, however, as they can be recorded in nonepileptic tissue and induced by cognitive, visual, or motor tasks. Consequently, efforts to distinguish between pathological and physiological HFOs have identified several traits specific to pathological HFOs, such as coupling with interictal spikes, association with delta waves, and stereotypical morphologies. On the opposite end of the EEG spectrum, sub-delta oscillations have been shown to co-localize with the seizure onset zones (SOZ) and appear in a narrower spatial distribution than activity in the conventional EEG frequency bands. In this report, we review studies that implicate HFOs and ISA in ictogenesis and discuss current limitations such as inter-observer variability and poor standardization of recording techniques. Furthermore, we propose that HFOs and ISA should be analyzed in addition to activity in the conventional EEG band during intracranial presurgical EEG monitoring to identify the best possible surgical margin.
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Affiliation(s)
- Somin Lee
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA; Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA
| | - Naoum P Issa
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Sandra Rose
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - James X Tao
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Peter C Warnke
- Department of Surgery, The University of Chicago, Chicago, IL, 60607, USA
| | - Vernon L Towle
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA; Department of Surgery, The University of Chicago, Chicago, IL, 60607, USA; Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60607, USA
| | - Wim van Drongelen
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA; Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA; Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA; Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60607, USA
| | - Shasha Wu
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA.
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Guo J, Yang K, Liu H, Yin C, Xiang J, Li H, Ji R, Gao Y. A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2474-2482. [PMID: 29994761 PMCID: PMC6299455 DOI: 10.1109/tmi.2018.2836965] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance.
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Wieser HG. Presurgical diagnosis of epilepsies – concepts and diagnostic tools. JOURNAL OF EPILEPTOLOGY 2016. [DOI: 10.1515/joepi-2016-0014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
SummaryIntroduction.Numerous reviews of the currently established concepts, strategies and diagnostic tools used in epilepsy surgery have been published. The focus concept which was initially developed by Forster, Penfield and Jasper and popularised and enriched by Lüders, is still fundamental for epilepsy surgery.Aim.To present different conceptual views of the focus concept and to discuss more recent network hypothesis, emphasizing so-called “critical modes of an epileptogenic circuit”.Method.A literature search was conducted using keywords: presurgical evaluation, epileptic focus concepts, cortical zones, diagnostic tools.Review and remarks.The theoretical concepts of the epileptic focus are opposed to the network hypothesis. The definitions of the various cortical zones have been conceptualized in the presurgical evaluation of candidates for epilepsy surgery: the seizure onset zone versus the epileptogenic zone, the symptomatogenic zone, the irritative and functional deficit zones are characterized. The epileptogenic lesion, the “eloquent cortex” and secondary epileptogenesis (mirror focus) are dealt with. The current diagnostic techniques used in the definition of these cortical zones, such as video-EEG monitoring, non-invasive and invasive EEG recording techniques, magnetic resonance imaging, ictal single photon emission computed tomography, and positron emission tomography, are discussed and illustrated. Potential modern surrogate markers of epileptogenicity, such asHigh frequency oscillations, Ictal slow waves/DC shifts, Magnetic resonance spectroscopy, Functional MRI,the use ofMagnetized nanoparticlesin MRI,Transcranial magnetic stimulation,Optical intrinsic signalimaging, andSeizure predictionare discussed. Particular emphasis is put on the EEG: Scalp EEG, semi-invasive and invasive EEG (Stereoelectroencephalography) and intraoperative electrocorticography are illustrated. Ictal SPECT and18F-FDG PET are very helpful and several other procedures, such as dipole source localization and spike-triggered functional MRI are already widely used. The most important lateralizing and localizing ictal signs and symptoms are summarized. It is anticipated that the other clinically valid surrogate markers of epileptogenesis and epileptogenicity will be further developed in the near future. Until then the concordance of the results of seizure semiology, localization of epileptogenicity by EEG and MRI remains the most important prerequisite for successful epilepsy surgery.Conclusions and future perspectives.Resective epilepsy surgery is a widely accepted and successful therapeutic approach, rendering up to 80% of selected patients seizure free. Although other therapies, such as radiosurgery, and responsive neurostimulation will increasingly play a role in patients with an unresectable lesion, it is unlikely that they will replace selective resective surgery. The hope is that new diagnostic techniques will be developed that permit more direct definition and measurement of the epileptogenic zone.
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Interictal high-frequency oscillations (HFOs) as predictors of high frequency and conventional seizure onset zones. Epileptic Disord 2016; 17:413-24. [PMID: 26620382 DOI: 10.1684/epd.2015.0774] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We investigated the relationship between the interictal high-frequency oscillations (HFOs) and the seizure onset zones (SOZs) defined by the ictal HFOs or conventional frequency activity (CFA), and evaluated the usefulness of the interictal HFOs as spatial markers of the SOZs. We analysed seizures showing discrete HFOs at onset on intracranial EEGs acquired at ≥1000-Hz sampling rate in a training cohort of 10 patients with temporal and extratemporal epilepsy. We classified each ictal channel as: HFO+ (HFOs at onset with subsequent evolution), HFO- (HFOs at onset without evolution), CFA (1.6-70-Hz activity at onset with evolution), or non-ictal. We defined the SOZs as: hSOZ (HFO+ channels only), hfo+&-SOZ (HFO+ and HFO- channels), and cSOZ (CFA channels). Using automated methods, we detected the interictal HFOs and extracted five features: density, connectivity, peak frequency, log power, and amplitude. We created logistic regression models using these features, and tested their performance in a separate replication cohort of three patients. The models containing the five interictal HFO features reliably differentiated the channels located inside the SOZ from those outside in the training cohort (p<0.001), reaching the highest accuracy for the classification of hSOZ. Log power and connectivity had the highest odds ratios, both being higher for the channels inside the SOZ compared with those outside the SOZ. In the replication cohort of novel patients, the same models differentiated the HFO+ from HFO- channels, and predicted the extents of the hSOZ and hfo+&-SOZ (F1 measure >0.5) but not the cSOZ. Our study shows that the interictal HFOs are useful in defining the spatial extent of the SOZ, and predicting whether or not a given channel in a novel patient would be involved in the seizure. The findings support the existence of an abnormal network of tightly-linked ictal and interictal HFOs in patients with intractable epilepsy.
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Davis MC, Broadwater DR, Mathews WH, Paige AL, DeWolfe JL, Elgavish RA, Riley KO, Ver Hoef LW. Statistical modeling of ICEEG features that determine resection planning. Clin Neurol Neurosurg 2016; 147:18-23. [PMID: 27249656 DOI: 10.1016/j.clineuro.2016.05.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 02/18/2016] [Accepted: 05/16/2016] [Indexed: 11/26/2022]
Abstract
OBJECT The interpretation of intracranial EEG (ICEEG) recordings is a complex balance of the significance of specific rhythms and their relative timing to seizure onset. Ictal and interictal findings are evaluated in light of findings from cortical stimulation of eloquent cortex to determine the area of resection. PATIENTS AND METHODS Patients with ICEEG electrodes and subsequent surgical resection were retrospectively identified. Only the first 15s of ictal activity, which was divided into five 3-s epochs, was considered. Every electrode in each patient was considered a separate observation in a logistic regression model to predict whether the cortex under a given electrode was included in the planned resection. RESULTS 19 included patients had a total of 37 unique seizures. Recordings from a total of 1306 electrodes were analyzed. The strongest predictors of resection of cortex underlying a given electrode was the presence of low-voltage fast activity in Epoch 1, rhythmic spikes in Epoch 1, interictal paroxysmal fast activity, and low-voltage fast activity in Epoch 2. High-amplitude beta spikes and rhythmic slow waves were also significant predictors in Epoch 1. Interictal spikes had a higher odds ratio of affecting the planned resection if described as "continuous" or "very frequent". The presence of motor or language cortex were the strongest negative predictors of resecting underlying cortex. CONCLUSIONS Here we describe a novel model of ictal and interictal patterns significantly associated with the inclusion of cortex underlying a given ICEEG electrode in the surgical resection plan.
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Affiliation(s)
- Matthew C Davis
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Devin R Broadwater
- University of Alabama at Birmingham School of Medical, Birmingham, AL, United States.
| | - Winn H Mathews
- School of Medicine, University of South Alabama, Mobile, AL, United States
| | - A Lebron Paige
- UAB Epilepsy Center, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jennifer L DeWolfe
- UAB Epilepsy Center, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Ro A Elgavish
- UAB Epilepsy Center, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Kristen O Riley
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Lawrence W Ver Hoef
- UAB Epilepsy Center, University of Alabama at Birmingham, Birmingham, AL, United States
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Chaitanya G, Sinha S, Narayanan M, Satishchandra P. Scalp high frequency oscillations (HFOs) in absence epilepsy: An independent component analysis (ICA) based approach. Epilepsy Res 2015. [DOI: 10.1016/j.eplepsyres.2015.06.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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