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Tveitstøl T, Tveter M, Pérez T. AS, Hatlestad-Hall C, Yazidi A, Hammer HL, Hebold Haraldsen IRJ. Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models. Front Neuroinform 2024; 17:1272791. [PMID: 38351907 PMCID: PMC10861709 DOI: 10.3389/fninf.2023.1272791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/07/2023] [Indexed: 02/16/2024] Open
Abstract
Introduction A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability and scalability of the DL models. Methods In this work, we propose a technique for handling such varied numbers of EEG channels by splitting the EEG montages into distinct regions and merge the channels within the same region to a region representation. The solution is termed Region Based Pooling (RBP). The procedure of splitting the montage into regions is performed repeatedly with different region configurations, to minimize potential loss of information. As RBP maps a varied number of EEG channels to a fixed number of region representations, both current and future DL architectures may apply RBP with ease. To demonstrate and evaluate the adequacy of RBP to handle a varied number of EEG channels, sex classification based solely on EEG was used as a test example. The DL models were trained on 129 channels, and tested on 32, 65, and 129-channels versions of the data using the same channel positions scheme. The baselines for comparison were zero-filling the missing channels and applying spherical spline interpolation. The performances were estimated using 5-fold cross validation. Results For the 32-channel system version, the mean AUC values across the folds were: RBP (93.34%), spherical spline interpolation (93.36%), and zero-filling (76.82%). Similarly, on the 65-channel system version, the performances were: RBP (93.66%), spherical spline interpolation (93.50%), and zero-filling (85.58%). Finally, the 129-channel system version produced the following results: RBP (94.68%), spherical spline interpolation (93.86%), and zero-filling (91.92%). Conclusion In conclusion, RBP obtained similar results to spherical spline interpolation, and superior results to zero-filling. We encourage further research and development of DL models in the cross-dataset setting, including the use of methods such as RBP and spherical spline interpolation to handle a varied number of EEG channels.
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Affiliation(s)
- Thomas Tveitstøl
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Mats Tveter
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ana S. Pérez T.
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | | | - Anis Yazidi
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
| | - Hugo L. Hammer
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Holistic Systems, SimulaMet, Oslo, Norway
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Erickson B, Rich R, Shankar S, Kim B, Driscoll N, Mentzelopoulos G, Fernandez-Nuñez G, Vitale F, Medaglia JD. Evaluating and benchmarking the EEG signal quality of high-density, dry MXene-based electrode arrays against gelled Ag/AgCl electrodes. J Neural Eng 2024; 21:016005. [PMID: 38081060 PMCID: PMC10788783 DOI: 10.1088/1741-2552/ad141e] [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: 06/14/2023] [Revised: 09/17/2023] [Accepted: 12/11/2023] [Indexed: 01/13/2024]
Abstract
Objective.To evaluate the signal quality of dry MXene-based electrode arrays (also termed 'MXtrodes') for electroencephalographic (EEG) recordings where gelled Ag/AgCl electrodes are a standard.Approach.We placed 4 × 4 MXtrode arrays and gelled Ag/AgCl electrodes on different scalp locations. The scalp was cleaned with alcohol and rewetted with saline before application. We recorded from both electrode types simultaneously while participants performed a vigilance task.Main results.The root mean squared amplitude of MXtrodes was slightly higher than that of Ag/AgCl electrodes (.24-1.94 uV). Most MXtrode pairs had slightly lower broadband spectral coherence (.05 to .1 dB) and Delta- and Theta-band timeseries correlation (.05 to .1 units) compared to the Ag/AgCl pair (p< .001). However, the magnitude of correlation and coherence was high across both electrode types. Beta-band timeseries correlation and spectral coherence were higher between neighboring MXtrodes in the array (.81 to .84 units) than between any other pair (.70 to .75 units). This result suggests the close spacing of the nearest MXtrodes (3 mm) more densely sampled high spatial-frequency topographies. Event-related potentials were more similar between MXtrodes (ρ⩾ .95) than equally spaced Ag/AgCl electrodes (ρ⩽ .77,p< .001). Dry MXtrode impedance (x̄= 5.15 KΩ cm2) was higher and more variable than gelled Ag/AgCl electrodes (x̄= 1.21 KΩ cm2,p< .001). EEG was also recorded on the scalp across diverse hair types.Significance.Dry MXene-based electrodes record EEG at a quality comparable to conventional gelled Ag/AgCl while requiring minimal scalp preparation and no gel. MXtrodes can record independent signals at a spatial density four times higher than conventional electrodes, including through hair, thus opening novel opportunities for research and clinical applications that could benefit from dry and higher-density configurations.
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Affiliation(s)
- Brian Erickson
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Ryan Rich
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Sneha Shankar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Brian Kim
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Nicolette Driscoll
- Laboratory of Electronics Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Guadalupe Fernandez-Nuñez
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John D Medaglia
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Drexel University, Philadelphia, PA 19104, United States of America
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Stoub TR, Stein MA, Bermeo-Ovalle A. Setting up EEG Source Imaging in Practice. J Clin Neurophysiol 2024; 41:50-55. [PMID: 38181387 DOI: 10.1097/wnp.0000000000001050] [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: 01/07/2024] Open
Abstract
SUMMARY Adding EEG source imaging to a clinical practice has clear advantages over visual inspection of EEG. This article offers insight on incorporating EEG source imaging into an EEG laboratory and the best practices for producing optimal source analysis results.
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Affiliation(s)
- Travis R Stoub
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, U.S.A
| | - Michael A Stein
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, U.S.A
| | - Adriana Bermeo-Ovalle
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, U.S.A
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Takagi S. Exploring Ripple Waves in the Human Brain. Clin EEG Neurosci 2023; 54:594-600. [PMID: 34287087 DOI: 10.1177/15500594211034371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Ripples are brief (<150 ms) high-frequency oscillatory neural activities in the brain with a range of 140 to 200 Hz in rodents and 80 to 140 Hz in humans. Ripples are regarded as playing an essential role in several aspects of memory function, mainly in the hippocampus. This type of ripple generally occurs with sharp waves and is called a sharp-wave ripple (SPW-R). Extensive research of SPW-Rs in the rodent brain while actively awake has also linked the function of these SPW-Rs to navigation and decision making. Although many studies with rodents unveiled SPW-R function, research in humans on this subject is still sparse. Therefore, unveiling SPW-R function in the human hippocampus is warranted. A certain type of ripples may also be a biomarker of epilepsy. This type of ripple is called a pathological ripple (p-ripple). p-ripples have a wider range of frequency (80-500 Hz) than SPW-Rs, and the range of frequency is especially higher in brain regions that are intrinsically linked to epilepsy onset. Brain regions producing ripples are too small for scalp electrode recording, and intracranial recording is typically needed to detect ripples. In addition, SPW-Rs in the human hippocampus have been recorded from patients with epilepsy who may have p-ripples. Differentiating SPW-Rs and p-ripples is often not easy. We need to develop more sophisticated methods to record SPW-Rs to differentiate them from p-ripples. This paper reviews the general features and roles of ripple waves.
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Affiliation(s)
- Shunsuke Takagi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Japan
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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Hatlestad-Hall C, Bruña R, Liljeström M, Renvall H, Heuser K, Taubøll E, Maestú F, Haraldsen IH. Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clin Neurophysiol 2023; 150:1-16. [PMID: 36972647 DOI: 10.1016/j.clinph.2023.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. METHODS EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. RESULTS The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. CONCLUSIONS Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SIGNIFICANCE Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.
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Affiliation(s)
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; BrainSymph AS, Oslo, Norway
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Kuhnke N, Wusthoff CJ, Swarnalingam E, Yanoussi M, Jacobs J. Epileptic high-frequency oscillations occur in neonates with a high risk for seizures. Front Neurol 2023; 13:1048629. [PMID: 36686542 PMCID: PMC9848430 DOI: 10.3389/fneur.2022.1048629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/30/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction Scalp high-frequency oscillations (HFOs, 80-250 Hz) are increasingly recognized as EEG markers of epileptic brain activity. It is, however, unclear what level of brain maturity is necessary to generate these oscillations. Many studies have reported the occurrence of scalp HFOs in children with a correlation between treatment success of epileptic seizures and the reduction of HFOs. More recent studies describe the reliable detection of HFOs on scalp EEG during the neonatal period. Methods In the present study, continuous EEGs of 38 neonates at risk for seizures were analyzed visually for the scalp HFOs using 30 min of quiet sleep EEG. EEGs of 14 patients were of acceptable quality to analyze HFOs. Results The average rate of HFOs was 0.34 ± 0.46/min. About 3.2% of HFOs occurred associated with epileptic spikes. HFOs were significantly more frequent in EEGs with abnormal vs. normal background activities (p = 0.005). Discussion Neonatal brains are capable of generating HFOs. HFO could be a viable biomarker for neonates at risk of developing seizures. Our preliminary data suggest that HFOs mainly occur in those neonates who have altered background activity. Larger data sets are needed to conclude whether HFO occurrence is linked to seizure generation and whether this might predict the development of epilepsy.
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Affiliation(s)
- Nicola Kuhnke
- Department of Pediatric Neurology and Muscular Disease, University Medical Center, Freiburg, Germany
| | | | - Eroshini Swarnalingam
- Department of Pediatrics, University of Calgary, Alberta Children's Hospital, Calgary, AB, Canada
| | - Mina Yanoussi
- Department of Pediatric Neurology and Muscular Disease, University Medical Center, Freiburg, Germany
| | - Julia Jacobs
- Department of Pediatrics, University of Calgary, Alberta Children's Hospital, Calgary, AB, Canada,*Correspondence: Julia Jacobs ✉
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Windhager PF, Marcu AV, Trinka E, Bathke A, Höller Y. Are High Frequency Oscillations in Scalp EEG Related to Age? Front Neurol 2022; 12:722657. [PMID: 35153968 PMCID: PMC8829347 DOI: 10.3389/fneur.2021.722657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND High-frequency oscillations (HFOs) have received much attention in recent years, particularly in the clinical context. In addition to their application as a marker for pathological changes in patients with epilepsy, HFOs have also been brought into context with several physiological mechanisms. Furthermore, recent studies reported a relation between an increase of HFO rate and age in invasive EEG recordings. The present study aimed to investigate whether this relation can be replicated in scalp-EEG. METHODS We recorded high-density EEG from 11 epilepsy patients at rest as well as during motor performance. Manual detection of HFOs was performed by two independent raters following a standardized protocol. Patients were grouped by age into younger (<25 years) and older (>50 years) participants. RESULTS No significant difference of HFO-rates was found between groups [U = 10.5, p = 0.429, r = 0.3]. CONCLUSIONS Lack of replicability of the age effect of HFOs may be due to the local propagation patterns of age-related HFOs occurring in deep structures. However, limitations such as small sample size, decreased signal-to-noise ratio as compared to invasive recordings, as well as HFO-mimicking artifacts must be considered.
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Affiliation(s)
- Philipp Franz Windhager
- Department of Neurology, Christian-Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,*Correspondence: Philipp Franz Windhager
| | - Adrian V. Marcu
- Department of Neurology, Christian-Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian-Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Arne Bathke
- Department of Mathematics, Paris Lodron University Salzburg, Salzburg, Austria
| | - Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland
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High-frequency oscillations in scalp EEG: A systematic review of methodological choices and clinical findings. Clin Neurophysiol 2022; 137:46-58. [DOI: 10.1016/j.clinph.2021.12.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 02/08/2023]
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Sun Y, Ren G, Ren J, Wang Q. High-frequency oscillations detected by electroencephalography as biomarkers to evaluate treatment outcome, mirror pathological severity and predict susceptibility to epilepsy. ACTA EPILEPTOLOGICA 2021. [DOI: 10.1186/s42494-021-00063-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractHigh-frequency oscillations (HFOs) in the electroencephalography (EEG) have been extensively investigated as a potential biomarker of epileptogenic zones. The understanding of the role of HFOs in epilepsy has been advanced considerably over the past decade, and the use of scalp EEG facilitates recordings of HFOs. HFOs were initially applied in large scale in epilepsy surgery and are now being utilized in other applications. In this review, we summarize applications of HFOs in 3 subtopics: (1) HFOs as biomarkers to evaluate epilepsy treatment outcome; (2) HFOs as biomarkers to measure seizure propensity; (3) HFOs as biomarkers to reflect the pathological severity of epilepsy. Nevertheless, knowledge regarding the above clinical applications of HFOs remains limited at present. Further validation through prospective studies is required for its reliable application in the clinical management of individual epileptic patients.
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Dukic S, McMackin R, Costello E, Metzger M, Buxo T, Fasano A, Chipika R, Pinto-Grau M, Schuster C, Hammond M, Heverin M, Coffey A, Broderick M, Iyer PM, Mohr K, Gavin B, McLaughlin R, Pender N, Bede P, Muthuraman M, van den Berg L, Hardiman O, Nasseroleslami B. Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis. Brain 2021; 145:621-631. [PMID: 34791079 PMCID: PMC9014749 DOI: 10.1093/brain/awab322] [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: 03/29/2021] [Revised: 06/25/2021] [Accepted: 07/26/2021] [Indexed: 11/14/2022] Open
Abstract
Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.
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Affiliation(s)
- Stefan Dukic
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland.,Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands
| | - Roisin McMackin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Emmet Costello
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Marjorie Metzger
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Teresa Buxo
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Antonio Fasano
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Rangariroyashe Chipika
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Marta Pinto-Grau
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Christina Schuster
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Michaela Hammond
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Mark Heverin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Amina Coffey
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Michael Broderick
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Parameswaran M Iyer
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Kieran Mohr
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Brighid Gavin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Russell McLaughlin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Niall Pender
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Muthuraman Muthuraman
- Movement disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Leonard van den Berg
- Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland.,Trinity College Institute of Neuroscience, Trinity College Dublin, University of Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
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Yang Y, Wang W, Wang J, Wang M, Li X, Yan Z, Deng Q, Feng X, Luan G, Yang X, Li T. Scalp-HFO indexes are biomarkers for the lateralization and localization of the epileptogenic zone in preoperative assessment. J Neurophysiol 2021; 126:1148-1158. [PMID: 34495792 DOI: 10.1152/jn.00212.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
During the noninvasive evaluation phase for refractory epilepsy, the localization of the epileptogenic zone (EZ) is essential for the surgical protocols. Confirmation of laterality is required when the preoperative evaluation limits the EZ to bilateral anterior temporal lobes or bilateral frontal lobes. High-frequency oscillations (HFOs) are considered to be promising biological markers for the EZ. However, a large number of studies on HFOs stem from intracranial research. There were few quantitative measures for scalp HFOs, so we proposed a new method to quantify and analyze scalp HFOs. This method was called the "scalp-HFO index" (HI) and calculated in both the EZ and non-EZ. The calculation was based on the numbers and spectral power of scalp HFOs automatically detected. We labeled the brain lobes involved in the EZ as regions of interest (ROIs). The HIs based on the ripple numbers (n-HI) and spectral power (s-HI) were significantly higher in the ROI than in the contra-ROI (P = 0.012, P = 0.003), indicating that HIs contributed to the lateralization of EZ. The sensitivity and specificity of n-HI for the localization of the EZ were 90% and 79.58%, respectively, suggesting that n-HI was valuable in localizing the EZ. HI may contribute to the implantation strategy of invasive electrodes. However, few scalp HFOs were recorded when the EZ was located in the medial cortex region.NEW & NOTEWORTHY We proposed the scalp-high-frequency oscillation (HFO) index (HI) as a quantitative assessment method for scalp HFOs to locate the epileptogenic zone (EZ). Our results showed that the HI in regions of interest (ROIs) was significantly higher than in contra-ROIs. Sensitivity and specificity of HI based on ripple rates (n-HI) for EZ localization were 90% and 79.58%, respectively. If the n-HI of the brain region was >1.35, it was more likely to be an epileptogenic region. Clinical application of HIs as an indicator may facilitate localization of the EZ.
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Affiliation(s)
- Yujiao Yang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Wei Wang
- 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.,Bioland Laboratory, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory, Guangzhou, 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
| | - Xiaonan Li
- 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.,Bioland Laboratory, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory, Guangzhou, China
| | - Zhaofen Yan
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Qinqin Deng
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Xing Feng
- Department of Neurology, 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, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory, Guangzhou, China
| | - Tianfu Li
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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Wang W, Li H, Yan J, Zhang H, Li X, Zheng S, Wang J, Xing Y, Cheng L, Li D, Lai H, Qu J, Loh HH, Fang F, Yang X. Automatic detection of interictal ripples on scalp EEG to evaluate the effect and prognosis of ACTH therapy in patients with infantile spasms. Epilepsia 2021; 62:2240-2251. [PMID: 34309835 DOI: 10.1111/epi.17018] [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: 03/01/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVE We aimed to explore the feasibility of using scalp-recorded high-frequency oscillations (HFOs) to evaluate the efficacy and prognosis of adrenocorticotropic hormone (ACTH) treatment in patients with infantile spasms. METHODS Thirty-nine children with infantile spasms were enrolled and divided into seizure-free and non-seizure-free groups after ACTH treatment. Patients who were seizure-free were further divided into relapse and non-relapse subgroups based on the observations made during a 6-month follow-up period. Scalp ripples were detected and compared during the interictal periods before and after 2 weeks of treatment. RESULTS After ACTH treatment, the number and channels of ripples were significantly lower, whereas the percentage decrease in the number, spectral power, and channels of ripples was significantly higher in the seizure-free group than in the non-seizure-free group. In addition, the relapse subgroup showed higher number and spectral power and wider distribution of ripples than did the non-relapse subgroup. Changes in HFOs in terms of number, spectral power, and channel of ripples were closely related to the severity of epilepsy and can indicate disease susceptibility. SIGNIFICANCE Scalp HFOs can be used as an effective biomarker to monitor the effect and evaluate the prognosis of ACTH therapy in patients with infantile spasms.
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Affiliation(s)
- Wei Wang
- Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Hua Li
- Department of Neurology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China
| | - Herui Zhang
- Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Xiaonan Li
- Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Su Zheng
- Department of Neurology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Jiaoyang Wang
- Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Yue Xing
- Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Lipeng Cheng
- Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Donghong Li
- The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Huanling Lai
- Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Junda Qu
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Horace H Loh
- Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Fang Fang
- Department of Neurology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
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14
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Dehnavi F, Koo-Poeggel PC, Ghorbani M, Marshall L. Spontaneous slow oscillation - slow spindle features predict induced overnight memory retention. Sleep 2021; 44:6277833. [PMID: 34003291 PMCID: PMC8503833 DOI: 10.1093/sleep/zsab127] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
Study Objectives Synchronization of neural activity within local networks and between brain regions is a major contributor to rhythmic field potentials such as the EEG. On the other hand, dynamic changes in microstructure and activity are reflected in the EEG, for instance slow oscillation (SO) slope can reflect synaptic strength. SO-spindle coupling is a measure for neural communication. It was previously associated with memory consolidation, but also shown to reveal strong interindividual differences. In studies, weak electric current stimulation has modulated brain rhythms and memory retention. Here, we investigate whether SO-spindle coupling and SO slope during baseline sleep are associated with (predictive of) stimulation efficacy on retention performance. Methods Twenty-five healthy subjects participated in three experimental sessions. Sleep-associated memory consolidation was measured in two sessions, in one anodal transcranial direct current stimulation oscillating at subjects individual SO frequency (so-tDCS) was applied during nocturnal sleep. The third session was without a learning task (baseline sleep). The dependence on SO-spindle coupling and SO-slope during baseline sleep of so-tDCS efficacy on retention performance were investigated. Results Stimulation efficacy on overnight retention of declarative memories was associated with nesting of slow spindles to SO trough in deep nonrapid eye movement baseline sleep. Steepness and direction of SO slope in baseline sleep were features indicative for stimulation efficacy. Conclusions Findings underscore a functional relevance of activity during the SO up-to-down state transition for memory consolidation and provide support for distinct consolidation mechanisms for types of declarative memories.
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Affiliation(s)
- Fereshteh Dehnavi
- Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Ping Chai Koo-Poeggel
- Institute of Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Ratzeburger Allee, Lübeck, Germany.,Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck
| | - Maryam Ghorbani
- Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.,Rayan Center for Neuroscience and Behavior, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Lisa Marshall
- Institute of Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Ratzeburger Allee, Lübeck, Germany.,Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck
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15
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Quintiliani M, Bianchi F, Fuggetta F, Chieffo DPR, Ramaglia A, Battaglia DI, Tamburrini G. Role of high-density EEG (hdEEG) in pre-surgical epilepsy evaluation in children: case report and review of the literature. Childs Nerv Syst 2021; 37:1429-1437. [PMID: 33604716 PMCID: PMC8084826 DOI: 10.1007/s00381-021-05069-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 02/02/2021] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Electrical source imaging (ESI) and especially hdEEG represent a noninvasive, low cost and accurate method of localizing epileptic zone (EZ). Such capability can greatly increase seizure freedom rate in surgically treated drug resistant epilepsy cases. Furthermore, ESI might be important in intracranial record planning. CASE REPORT We report the case of a 15 years old boy suffering from drug resistant epilepsy with a previous history of DNET removal. The patient suffered from heterogeneous seizure semiology characterized by anesthesia and loss of tone in the left arm, twisting of the jaw to the left and dysarthria accompanied by daze; lightheadedness sometimes associated with headache and dizziness and at a relatively short time distance negative myoclonus involving the left hand. Clinical evidence poorly match scalp and video EEG monitoring thus requiring hdEEG recording followed by SEEG to define surgical target. Surgery was also guided by ECoG and obtained seizure freedom. DISCUSSION ESI offers an excellent estimate of EZ, being hdEEG and intracranial recordings especially important in defining it. We analyzed our results together with the data from the literature showing how in children hdEEG might be even more crucial than in adults due to the heterogeneity in seizures phenomenology. The complexity of each case and the technical difficulties in dealing with children, stress even more the importance of a noninvasive tool for diagnosis. In fact, hdEEG not only guided in the presented case SEEG planning but may also in the future offer the possibility to replace it.
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Affiliation(s)
- Michela Quintiliani
- Infantile Neuropsychiatry, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Federico Bianchi
- Pediatric Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168, Rome, Italy.
| | - Filomena Fuggetta
- Infantile Neuropsychiatry, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | | | - Antonia Ramaglia
- Institute of Radiology, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Domenica Immacolata Battaglia
- Infantile Neuropsychiatry, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gianpiero Tamburrini
- Pediatric Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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16
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Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources. Proc Natl Acad Sci U S A 2021; 118:2011130118. [PMID: 33875582 PMCID: PMC8092606 DOI: 10.1073/pnas.2011130118] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Millions of people affected by epilepsy may undergo surgical resection of the epileptic tissues to stop seizures if such epileptic foci can be accurately delineated. High-frequency oscillations (HFOs), existing in electroencephalography, are highly correlated with epileptic brain, which is promising for guiding successful neurosurgery. However, it is unclear whether and how pathological HFOs can be differentiated to localize the epileptogenic tissues given the presence of various nonepileptic high-frequency activities. Here, we show morphological and source imaging evidence that pathological HFOs can be identified by the concurrence of epileptiform spikes. We describe a framework to delineate the underlying epileptogenicity using this biomarker. Our work may offer translational tools to improve treatments by noninvasively demarking pathological activities and hence epileptic foci. High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.
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17
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McCrimmon CM, Riba A, Garner C, Maser AL, Phillips DJ, Steenari M, Shrey DW, Lopour BA. Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms. J Neural Eng 2021; 18. [PMID: 33217752 DOI: 10.1088/1741-2552/abcc7e] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 11/20/2020] [Indexed: 11/11/2022]
Abstract
Objective.Scalp high-frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Here we set out to robustly characterize the rate and spatial distribution of HFOs in large datasets from IS subjects using fully automated HFO detection techniques.Approach.We prospectively collected long-term scalp EEG data from 12 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adrenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 h for controls and 84.5 h for IS subjects (∼1300 h total). Ripples (80-250 Hz) were detected in all EEG data using an automated algorithm.Main results.HFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5 min-1and 2.9 min-1, respectively;p = 0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98 min-1and 0.82 min-1, respectively). Spatially, IS patients exhibited significantly higher rates of HFOs in the posterior parasaggital region and significantly lower HFO rates in frontal channels, and this difference was more pronounced during sleep. In IS subjects, ACTH therapy significantly decreased the rate of HFOs.Significance.Here we provide a detailed characterization of the spatial distribution and rates of HFOs associated with IS, which may have relevance for diagnosis and assessment of treatment response. We also demonstrate that our fully automated algorithm can be used to detect HFOs in long-term scalp EEG with sufficient accuracy to clearly discriminate healthy subjects from those with IS.
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Affiliation(s)
- Colin M McCrimmon
- Medical Scientist Training Program, University of California, Irvine, CA 92617, United States of America.,Department Neurology, University of California, Los Angeles, CA 90095, United States of America
| | - Aliza Riba
- Division Neurology, Children's Hospital of Orange County, Orange, CA 92868, United States of America
| | - Cristal Garner
- Division Neurology, Children's Hospital of Orange County, Orange, CA 92868, United States of America
| | - Amy L Maser
- Department Psychology, Children's Hospital of Orange County, Orange, CA 92868, United States of America
| | - Donald J Phillips
- Division Neurology, Children's Hospital of Orange County, Orange, CA 92868, United States of America.,Department Pediatrics, University of California, Irvine, Irvine, CA 92617, United States of America
| | - Maija Steenari
- Division Neurology, Children's Hospital of Orange County, Orange, CA 92868, United States of America.,Department Pediatrics, University of California, Irvine, Irvine, CA 92617, United States of America
| | - Daniel W Shrey
- Division Neurology, Children's Hospital of Orange County, Orange, CA 92868, United States of America.,Department Pediatrics, University of California, Irvine, Irvine, CA 92617, United States of America
| | - Beth A Lopour
- Department Biomedical Engineering, University of California, Irvine, Irvine, CA 92617, United States of America
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18
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Bruder JC, Schmelzeisen C, Lachner-Piza D, Reinacher P, Schulze-Bonhage A, Jacobs J. Physiological Ripples Associated With Sleep Spindles Can Be Identified in Patients With Refractory Epilepsy Beyond Mesio-Temporal Structures. Front Neurol 2021; 12:612293. [PMID: 33643198 PMCID: PMC7902925 DOI: 10.3389/fneur.2021.612293] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 01/11/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: High frequency oscillations (HFO) are promising biomarkers of epileptic tissue. While group analysis suggested a correlation between surgical removal of HFO generating tissue and seizure free outcome, HFO could not predict seizure outcome on an individual patient level. One possible explanation is the lack of differentiation between physiological and epileptic HFO. In the mesio-temporal lobe, a proportion of physiological ripples can be identified by their association with scalp sleep spindles. Spike associated ripples in contrast can be considered epileptic. This study investigated whether categorizing ripples by the co-occurrence with sleep spindles or spikes improves outcome prediction after surgery. Additionally, it aimed to investigate whether spindle-ripple association is limited to the mesio-temporal lobe structures or visible across the whole brain. Methods: We retrospectively analyzed EEG of 31 patients with chronic intracranial EEG. Sleep spindles in scalp EEG and ripples and epileptic spikes in iEEG were automatically detected. Three ripple subtypes were obtained: SpindleR, Non-SpindleR, and SpikeR. Rate ratios between removed and non-removed brain areas were calculated. We compared the distinct ripple subtypes and their rates in different brain regions, inside and outside seizure onset areas and between patients with good and poor seizure outcome. Results: SpindleR were found across all brain regions. SpikeR had significantly higher rates in the SOZ than in Non-SOZ channels. A significant positive correlation between removal of ripple-events and good outcome was found for the mixed ripple group (rs = 0.43, p = 0.017) and for ripples not associated with spindles (rs=0.40, p = 0.044). Also, a significantly high proportion of spikes associated with ripples were removed in seizure free patients (p = 0.036). Discussion: SpindleR are found in mesio-temporal and neocortical structures, indicating that ripple-spindle-coupling might have functional importance beyond mesio-temporal structures. Overall, the proportion of SpindleR was low and separating spindle and spike associated ripples did not improve outcome prediction in our patient group. SpindleR analysis therefore can be a tool to identify physiological events but needs to be used in combination with other methods to have clinical relevance.
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Affiliation(s)
- Jonas C Bruder
- Department of Neuropediatrics and Muscular Disease, University Medical Center, Freiburg, Germany
| | - Christoph Schmelzeisen
- Department of Neuropediatrics and Muscular Disease, University Medical Center, Freiburg, Germany
| | - Daniel Lachner-Piza
- Department of Neuropediatrics and Muscular Disease, University Medical Center, Freiburg, Germany
| | - Peter Reinacher
- Stereotactic & Functional Neurosurgery, University Medical Center, Freiburg, Germany
| | | | - Julia Jacobs
- Department of Neuropediatrics and Muscular Disease, University Medical Center, Freiburg, Germany.,Epilepsy Center, University Medical Center, Freiburg, Germany
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Abstract
PURPOSE OF REVIEW Epilepsy surgery is the therapy of choice for 30-40% of people with focal drug-resistant epilepsy. Currently only ∼60% of well selected patients become postsurgically seizure-free underlining the need for better tools to identify the epileptogenic zone. This article reviews the latest neurophysiological advances for EZ localization with emphasis on ictal EZ identification, interictal EZ markers, and noninvasive neurophysiological mapping procedures. RECENT FINDINGS We will review methods for computerized EZ assessment, summarize computational network approaches for outcome prediction and individualized surgical planning. We will discuss electrical stimulation as an option to reduce the time needed for presurgical work-up. We will summarize recent research regarding high-frequency oscillations, connectivity measures, and combinations of multiple markers using machine learning. This latter was shown to outperform single markers. The role of NREM sleep for best identification of the EZ interictally will be discussed. We will summarize recent large-scale studies using electrical or magnetic source imaging for clinical decision-making. SUMMARY New approaches based on technical advancements paired with artificial intelligence are on the horizon for better EZ identification. They are ultimately expected to result in a more efficient, less invasive, and less time-demanding presurgical investigation.
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20
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Chen Z, Maturana MI, Burkitt AN, Cook MJ, Grayden DB. High-Frequency Oscillations in Epilepsy: What Have We Learned and What Needs to be Addressed. Neurology 2021; 96:439-448. [PMID: 33408149 DOI: 10.1212/wnl.0000000000011465] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 11/10/2020] [Indexed: 11/15/2022] Open
Abstract
For the past 2 decades, high-frequency oscillations (HFOs) have been enthusiastically studied by the epilepsy community. Emerging evidence shows that HFOs harbor great promise to delineate epileptogenic brain areas and possibly predict the likelihood of seizures. Investigations into HFOs in clinical epilepsy have advanced from small retrospective studies relying on visual identification and correlation analysis to larger prospective assessments using automatic detection and prediction strategies. Although most studies have yielded promising results, some have revealed significant obstacles to clinical application of HFOs, thus raising debate about the reliability and practicality of HFOs as clinical biomarkers. In this review, we give an overview of the current state of HFO research and pinpoint the conceptual and methodological issues that have hampered HFO translation. We highlight recent insights gained from long-term data, high-density recordings, and multicenter collaborations and discuss the open questions that need to be addressed in future research.
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Affiliation(s)
- Zhuying Chen
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia.
| | - Matias I Maturana
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
| | - Anthony N Burkitt
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
| | - Mark J Cook
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
| | - David B Grayden
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
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21
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Fan Y, Dong L, Liu X, Wang H, Liu Y. Recent advances in the noninvasive detection of high-frequency oscillations in the human brain. Rev Neurosci 2020; 32:305-321. [PMID: 33661582 DOI: 10.1515/revneuro-2020-0073] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/23/2020] [Indexed: 01/10/2023]
Abstract
In recent decades, a significant body of evidence based on invasive clinical research has showed that high-frequency oscillations (HFOs) are a promising biomarker for localization of the seizure onset zone (SOZ), and therefore, have the potential to improve postsurgical outcomes in patients with epilepsy. Emerging clinical literature has demonstrated that HFOs can be recorded noninvasively using methods such as scalp electroencephalography (EEG) and magnetoencephalography (MEG). Not only are HFOs considered to be a useful biomarker of the SOZ, they also have the potential to gauge disease severity, monitor treatment, and evaluate prognostic outcomes. In this article, we review recent clinical research on noninvasively detected HFOs in the human brain, with a focus on epilepsy. Noninvasively detected scalp HFOs have been investigated in various types of epilepsy. HFOs have also been studied noninvasively in other pathologic brain disorders, such as migraine and autism. Herein, we discuss the challenges reported in noninvasive HFO studies, including the scarcity of MEG and high-density EEG equipment in clinical settings, low signal-to-noise ratio, lack of clinically approved automated detection methods, and the difficulty in differentiating between physiologic and pathologic HFOs. Additional studies on noninvasive recording methods for HFOs are needed, especially prospective multicenter studies. Further research is fundamental, and extensive work is needed before HFOs can routinely be assessed in clinical settings; however, the future appears promising.
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Affiliation(s)
- Yuying Fan
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Liping Dong
- Library of China Medical University, Shenyang, China
| | - Xueyan Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hua Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yunhui Liu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
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Fast oscillations >40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography. Clin Neurophysiol 2020; 132:568-580. [PMID: 33450578 DOI: 10.1016/j.clinph.2020.11.031] [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] [Received: 06/09/2020] [Revised: 10/04/2020] [Accepted: 11/06/2020] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Fast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification. METHODS We analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity. RESULTS We identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively. CONCLUSIONS FO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches. SIGNIFICANCE This study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ.
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Jacobs J, Zijlmans M. HFO to Measure Seizure Propensity and Improve Prognostication in Patients With Epilepsy. Epilepsy Curr 2020; 20:338-347. [PMID: 33081501 PMCID: PMC7818207 DOI: 10.1177/1535759720957308] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The study of high frequency oscillations (HFO) in the electroencephalogram (EEG)
as biomarkers of epileptic activity has merely focused on their spatial location
and relationship to the epileptogenic zone. It has been suggested in several
ways that the amount of HFO at a certain point in time may reflect the disease
activity or severity. This could be clinically useful in several ways,
especially as noninvasive recording of HFO appears feasible. We grouped the
potential hypotheses into 4 categories: (1) HFO as biomarkers to predict the
development of epilepsy; (2) HFO as biomarkers to predict the occurrence of
seizures; (3) HFO as biomarkers linked to the severity of epilepsy, and (4) HFO
as biomarkers to evaluate outcome of treatment. We will review the literature
that addresses these 4 hypotheses and see to what extent HFO can be used to
measure seizure propensity and help determine prognosis of this unpredictable
disease.
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Affiliation(s)
- Julia Jacobs
- 157744Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
| | - Maeike Zijlmans
- 36512UMC Utrecht Brain Center Rudolf Magnus, Utrecht, the Netherlands
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Tamilia E, Dirodi M, Alhilani M, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery. Ann Clin Transl Neurol 2020; 7:329-342. [PMID: 32096612 PMCID: PMC7086004 DOI: 10.1002/acn3.50994] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/29/2020] [Accepted: 01/30/2020] [Indexed: 12/11/2022] Open
Abstract
Objective To assess the ability of high‐density Electroencephalography (HD‐EEG) and magnetoencephalography (MEG) to localize interictal ripples, distinguish between ripples co‐occurring with spikes (ripples‐on‐spike) and independent from spikes (ripples‐alone), and evaluate their localizing value as biomarkers of epileptogenicity in children with medically refractory epilepsy. Methods We retrospectively studied 20 children who underwent epilepsy surgery. We identified ripples on HD‐EEG and MEG data, localized their generators, and compared them with intracranial EEG (icEEG) ripples. When ripples and spikes co‐occurred, we performed source imaging distinctly on the data above 80 Hz (to localize ripples) and below 70 Hz (to localize spikes). We assessed whether missed resection of ripple sources predicted poor outcome, separately for ripples‐on‐spikes and ripples‐alone. Similarly, predictive value of spikes was calculated. Results We observed scalp ripples in 16 patients (10 good outcome). Ripple sources were highly concordant to the icEEG ripples (HD‐EEG concordance: 79%; MEG: 83%). When ripples and spikes co‐occurred, their sources were spatially distinct in 83‐84% of the cases. Removing the sources of ripples‐on‐spikes predicted good outcome with 90% accuracy for HD‐EEG (P = 0.008) and 86% for MEG (P = 0.044). Conversely, removing ripples‐alone did not predict outcome. Resection of spike sources (generated at the same time as ripples) predicted good outcome for HD‐EEG (P = 0.036; accuracy = 87%), while did not reach significance for MEG (P = 0.1; accuracy = 80%). Interpretation HD‐EEG and MEG localize interictal ripples with high precision in children with refractory epilepsy. Scalp ripples‐on‐spikes are prognostic, noninvasive biomarkers of epileptogenicity, since removing their cortical generators predicts good outcome. Conversely, scalp ripples‐alone are most likely generated by non‐epileptogenic areas.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Matilde Dirodi
- G. Tec Medical Engineering GmbHGuger Technologies OGGrazAustria
| | - Michel Alhilani
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - P. Ellen Grant
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Joseph R. Madsen
- Division of Epilepsy SurgeryDepartment of NeurosurgeryBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalHarvard Medical SchoolBostonMassachusetts
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical NeurophysiologyDepartment of NeurologyBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Christos Papadelis
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTexas
- School of MedicineTexas Christian University and University of North Texas Health Science CenterFort WorthTexas
- Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTexas
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Boran E, Sarnthein J, Krayenbühl N, Ramantani G, Fedele T. High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy. Sci Rep 2019; 9:16560. [PMID: 31719543 PMCID: PMC6851354 DOI: 10.1038/s41598-019-52700-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 10/17/2019] [Indexed: 11/10/2022] Open
Abstract
High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.
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Affiliation(s)
- Ece Boran
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH Zürich, Zürich, Switzerland
| | - Niklaus Krayenbühl
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Pädiatrische Neurochirurgie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Georgia Ramantani
- Neuropädiatrie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Tommaso Fedele
- Institute of Cognitive Neuroscience, Higher School of Economics - National Research University, Moscow, Russian Federation.
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In search of epileptic scalp high-frequency oscillations. Clin Neurophysiol 2019; 130:1172-1174. [PMID: 31064718 DOI: 10.1016/j.clinph.2019.04.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 04/11/2019] [Indexed: 11/20/2022]
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Michel CM, Brunet D. EEG Source Imaging: A Practical Review of the Analysis Steps. Front Neurol 2019; 10:325. [PMID: 31019487 PMCID: PMC6458265 DOI: 10.3389/fneur.2019.00325] [Citation(s) in RCA: 275] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 03/15/2019] [Indexed: 11/13/2022] Open
Abstract
The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
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Affiliation(s)
- Christoph M. Michel
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
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Thomschewski A, Hincapié AS, Frauscher B. Localization of the Epileptogenic Zone Using High Frequency Oscillations. Front Neurol 2019; 10:94. [PMID: 30804887 PMCID: PMC6378911 DOI: 10.3389/fneur.2019.00094] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 01/23/2019] [Indexed: 01/22/2023] Open
Abstract
For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40-50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.
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Affiliation(s)
- Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria,Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Ana-Sofía Hincapié
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada,*Correspondence: Birgit Frauscher
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