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Marino M, Mantini D. Human brain imaging with high-density electroencephalography: Techniques and applications. J Physiol 2024. [PMID: 39173191 DOI: 10.1113/jp286639] [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: 05/04/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Abstract
Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.
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Affiliation(s)
- Marco Marino
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Department of General Psychology, University of Padua, Padua, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Belgium
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Schulze-Bonhage A, Nitsche MA, Rotter S, Focke NK, Rao VR. Neurostimulation targeting the epileptic focus: Current understanding and perspectives for treatment. Seizure 2024; 117:183-192. [PMID: 38452614 DOI: 10.1016/j.seizure.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/09/2024] Open
Abstract
For the one third of people with epilepsy whose seizures are not controlled with medications, targeting the seizure focus with neurostimulation can be an effective therapeutic strategy. In this focused review, we summarize a discussion of targeted neurostimulation modalities during a workshop held in Frankfurt, Germany in September 2023. Topics covered include: available devices for seizure focus stimulation; alternating current (AC) and direct current (DC) stimulation to reduce focal cortical excitability; modeling approaches to simulate DC stimulation; reconciling the efficacy of focal stimulation with the network theory of epilepsy; and the emerging concept of 'neurostimulation zones,' which are defined as cortical regions where focal stimulation is most effective for reducing seizures and which may or may not directly involve the seizure onset zone. By combining experimental data, modeling results, and clinical outcome analysis, rational selection of target regions and stimulation parameters is increasingly feasible, paving the way for a broader use of neurostimulation for epilepsy in the future.
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Affiliation(s)
- Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center, University of Freiburg, Germany; European Reference Network EpiCare, Belgium; NeuroModul Basic, University of Freiburg, Freiburg, Germany.
| | - Michael A Nitsche
- Dept. Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy, Germany; German Center for Mental Health (DZPG), Germany
| | - Stefan Rotter
- Bernstein Center Freiburg & Faculty of Biology, University of Freiburg, Germany
| | - Niels K Focke
- Epilepsy Center, Clinic for Neurology, University Medical Center Göttingen, Germany
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, USA
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Ming Z, Chen D, Gao T, Tang Y, Tu W, Chen J. V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges. Neural Netw 2024; 172:106136. [PMID: 38266472 DOI: 10.1016/j.neunet.2024.106136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 11/20/2023] [Accepted: 01/16/2024] [Indexed: 01/26/2024]
Abstract
Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.
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Affiliation(s)
- Zhekai Ming
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Dan Chen
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China.
| | - Tengfei Gao
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Yunbo Tang
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
| | - Weiping Tu
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Jingying Chen
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
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Antal DC, Altenmüller DM, Dümpelmann M, Scheiwe C, Reinacher PC, Crihan ET, Ignat BE, Cuciureanu ID, Demerath T, Urbach H, Schulze-Bonhage A, Heers M. Semiautomated electric source imaging determines epileptogenicity of encephaloceles in temporal lobe epilepsy. Epilepsia 2024; 65:651-663. [PMID: 38258618 DOI: 10.1111/epi.17879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
OBJECTIVE We aimed to assess the ability of semiautomated electric source imaging (ESI) from long-term video-electroencephalographic (EEG) monitoring (LTM) to determine the epileptogenicity of temporopolar encephaloceles (TEs) in patients with temporal lobe epilepsy. METHODS We conducted a retrospective study involving 32 temporal lobe epilepsy patients with TEs as potentially epileptogenic lesions in structural magnetic resonance imaging scans. Findings were validated through invasive intracerebral stereo-EEG in six of 32 patients and postsurgical outcome after tailored resection of the TE in 17 of 32 patients. LTM (mean duration = 6 days) was performed using the 10/20 system with additional T1/T2 for all patients and sphenoidal electrodes in 23 of 32 patients. Semiautomated detection and clustering of interictal epileptiform discharges (IEDs) were carried out to create IED types. ESI was performed on the averages of the two most frequent IED types per patient, utilizing individual head models, and two independent inverse methods (sLORETA [standardized low-resolution brain electromagnetic tomography], MUSIC [multiple signal classification]). ESI maxima concordance and propagation in spatial relation to TEs were quantified for sources with good signal quality (signal-to-noise ratio > 2, explained signal > 60%). RESULTS ESI maxima correctly colocalized with a TE in 20 of 32 patients (62.5%) either at the onset or half-rising flank of at least one IED type per patient. ESI maxima showed propagation from the temporal pole to other temporal or extratemporal regions in 14 of 32 patients (44%), confirming propagation originating in the area of the TE. The findings from both inverse methods validated each other in 14 of 20 patients (70%), and sphenoidal electrodes exhibited the highest signal amplitudes in 17 of 23 patients (74%). The concordance of ESI with the TE predicted a seizure-free postsurgical outcome (Engel I vs. >I) with a diagnostic odds ratio of 2.1. SIGNIFICANCE Semiautomated ESI from LTM often successfully identifies the epileptogenicity of TEs and the IED onset zone within the area of the TEs. Additionally, it shows potential predictive power for postsurgical outcomes in these patients.
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Affiliation(s)
- Dorin-Cristian Antal
- Faculty of Medicine, Epilepsy Center, Medical Center-University of Freiburg, Freiburg, Germany
- Neurology Clinic, Rehabilitation Clinical Hospital, Iași, Romania
- I Neurology Clinic, "Prof. Dr. N. Oblu" Emergency Clinical Hospital, Iasi, Romania
- University of Medicine and Pharmacy "Grigore T. Popa", Iasi, Romania
| | | | - Matthias Dümpelmann
- Faculty of Medicine, Epilepsy Center, Medical Center-University of Freiburg, Freiburg, Germany
| | - Christian Scheiwe
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Peter C Reinacher
- Department of Stereotactic and Functional Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Fraunhofer Institute for Laser Technology, Aachen, Germany
| | | | - Bogdan-Emilian Ignat
- Neurology Clinic, Rehabilitation Clinical Hospital, Iași, Romania
- University of Medicine and Pharmacy "Grigore T. Popa", Iasi, Romania
| | - Iulian-Dan Cuciureanu
- I Neurology Clinic, "Prof. Dr. N. Oblu" Emergency Clinical Hospital, Iasi, Romania
- University of Medicine and Pharmacy "Grigore T. Popa", Iasi, Romania
| | - Theo Demerath
- Department of Neuroradiology, University Hospital Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, University Hospital Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Faculty of Medicine, Epilepsy Center, Medical Center-University of Freiburg, Freiburg, Germany
| | - Marcel Heers
- Faculty of Medicine, Epilepsy Center, Medical Center-University of Freiburg, Freiburg, Germany
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Mohammed AH, Cabrerizo M, Pinzon A, Yaylali I, Jayakar P, Adjouadi M. Graph neural networks in EEG spike detection. Artif Intell Med 2023; 145:102663. [PMID: 37925203 DOI: 10.1016/j.artmed.2023.102663] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 06/06/2023] [Accepted: 09/14/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. METHODS Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. RESULTS On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. CONCLUSION Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. SIGNIFICANCE This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.
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Affiliation(s)
- Ahmed Hossam Mohammed
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA.
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
| | - Alberto Pinzon
- Epilepsy Center, Baptist Hospital of Miami, 9090 SW 87th Ct Suite201, Miami, 33176, FL, USA
| | - Ilker Yaylali
- Department of Neurology, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, 97239, OR, USA
| | - Prasanna Jayakar
- Brain Institute, Nicklaus Children's Hospital, 3100 SW 62nd Ave, Miami, FL 33155, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
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Heide E, van de Velden D, Garnica Agudelo D, Hewitt M, Riedel C, Focke NK. Feasibility of high-density electric source imaging in the presurgical workflow: Effect of number of spikes and automated spike detection. Epilepsia Open 2023; 8:785-796. [PMID: 36938790 PMCID: PMC10472417 DOI: 10.1002/epi4.12732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 03/16/2023] [Indexed: 03/21/2023] Open
Abstract
OBJECTIVE Presurgical high-density electric source imaging (hdESI) of interictal epileptic discharges (IEDs) is only used by few epilepsy centers. One obstacle is the time-consuming workflow both for recording as well as for visual review. Therefore, we analyzed the effect of (a) an automated IED detection and (b) the number of IEDs on the accuracy of hdESI and time-effectiveness. METHODS In 22 patients with pharmacoresistant focal epilepsy receiving epilepsy surgery (Engel 1) we retrospectively detected IEDs both visually and semi-automatically using the EEG analysis software Persyst in 256-channel EEGs. The amount of IEDs, the Euclidean distance between hdESI maximum and resection zone, and the operator time were compared. Additionally, we evaluated the intra-individual effect of IED quantity on the distance between hdESI maximum of all IEDs and hdESI maximum when only a reduced amount of IEDs were included. RESULTS There was no significant difference in the number of IEDs between visually versus semi-automatically marked IEDs (74 ± 56 IEDs/patient vs 116 ± 115 IEDs/patient). The detection method of the IEDs had no significant effect on the mean distances between resection zone and hdESI maximum (visual: 26.07 ± 31.12 mm vs semi-automated: 33.6 ± 34.75 mm). However, the mean time needed to review the full datasets semi-automatically was shorter by 275 ± 46 min (305 ± 72 min vs 30 ± 26 min, P < 0.001). The distance between hdESI of the full versus reduced amount of IEDs of the same patient was smaller than 1 cm when at least a mean of 33 IEDs were analyzed. There was a significantly shorter intraindividual distance between resection zone and hdESI maximum when 30 IEDs were analyzed as compared to the analysis of only 10 IEDs (P < 0.001). SIGNIFICANCE Semi-automatized processing and limiting the amount of IEDs analyzed (~30-40 IEDs per cluster) appear to be time-saving clinical tools to increase the practicability of hdESI in the presurgical work-up.
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Affiliation(s)
- Ev‐Christin Heide
- Department of NeurologyUniversity Medical Center, Georg‐August UniversityGöttingenGermany
| | - Daniel van de Velden
- Department of NeurologyUniversity Medical Center, Georg‐August UniversityGöttingenGermany
| | - David Garnica Agudelo
- Department of NeurologyUniversity Medical Center, Georg‐August UniversityGöttingenGermany
| | - Manuel Hewitt
- Department of NeurologyUniversity Medical Center, Georg‐August UniversityGöttingenGermany
| | - Christian Riedel
- Institute for Diagnostic and Interventional NeuroradiologyUniversity Medical Center, Georg‐August UniversityGöttingenGermany
| | - Niels K. Focke
- Department of NeurologyUniversity Medical Center, Georg‐August UniversityGöttingenGermany
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Santalucia R, Carapancea E, Vespa S, Germany Morrison E, Ghasemi Baroumand A, Vrielynck P, Fierain A, Joris V, Raftopoulos C, Duprez T, Ferrao Santos S, van Mierlo P, El Tahry R. Clinical added value of interictal automated electrical source imaging in the presurgical evaluation of MRI-negative epilepsy: A real-life experience in 29 consecutive patients. Epilepsy Behav 2023; 143:109229. [PMID: 37148703 DOI: 10.1016/j.yebeh.2023.109229] [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: 02/06/2023] [Revised: 04/09/2023] [Accepted: 04/20/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE During the presurgical evaluation, manual electrical source imaging (ESI) provides clinically useful information in one-third of the patients but it is time-consuming and requires specific expertise. This prospective study aims to assess the clinical added value of a fully automated ESI analysis in a cohort of patients with MRI-negative epilepsy and describe its diagnostic performance, by evaluating sublobar concordance with stereo-electroencephalography (SEEG) results and surgical resection and outcome. METHODS All consecutive patients referred to the Center for Refractory Epilepsy (CRE) of St-Luc University Hospital (Brussels, Belgium) for presurgical evaluation between 15/01/2019 and 31/12/2020 meeting the inclusion criteria, were recruited to the study. Interictal ESI was realized on low-density long-term EEG monitoring (LD-ESI) and, whenever available, high-density EEG (HD-ESI), using a fully automated analysis (Epilog PreOp, Epilog NV, Ghent, Belgium). The multidisciplinary team (MDT) was asked to formulate hypotheses about the epileptogenic zone (EZ) location at sublobar level and make a decision on further management for each patient at two distinct moments: i) blinded to ESI and ii) after the presentation and clinical interpretation of ESI. Results leading to a change in clinical management were considered contributive. Patients were followed up to assess whether these changes lead to concordant results on stereo-EEG (SEEG) or successful epilepsy surgery. RESULTS Data from all included 29 patients were analyzed. ESI led to a change in the management plan in 12/29 patients (41%). In 9/12 (75%), modifications were related to a change in the plan of the invasive recording. In 8/9 patients, invasive recording was performed. In 6/8 (75%), the intracranial EEG recording confirmed the localization of the ESI at a sublobar level. So far, 5/12 patients, for whom the management plan was changed after ESI, were operated on and have at least one-year postoperative follow-up. In all cases, the EZ identified by ESI was included in the resection zone. Among these patients, 4/5 (80%) are seizure-free (ILAE 1) and one patient experienced a seizure reduction of more than 50% (ILAE 4). CONCLUSIONS In this single-center prospective study, we demonstrated the added value of automated ESI in the presurgical evaluation of MRI-negative cases, especially in helping to plan the implantation of depth electrodes for SEEG, provided that ESI results are integrated into the whole multimodal evaluation and clinically interpreted.
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Affiliation(s)
- Roberto Santalucia
- Cliniques Universitaires Saint-Luc, Paediatric Neurology Unit, Brussels, Belgium; Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Centre Hospitalier Neurologique William Lennox (CHNWL), Clinical Neurophysiology, Ottignies, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium.
| | - Evelina Carapancea
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium
| | - Simone Vespa
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium
| | - Enrique Germany Morrison
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium
| | - Amir Ghasemi Baroumand
- Medical Image and Signal Processing, Ghent University, Ghent, Belgium; Epilog NV, Ghent, Belgium
| | - Pascal Vrielynck
- Centre Hospitalier Neurologique William Lennox (CHNWL), Clinical Neurophysiology, Ottignies, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium
| | - Alexane Fierain
- Centre Hospitalier Neurologique William Lennox (CHNWL), Clinical Neurophysiology, Ottignies, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurology Unit, Brussels, Belgium
| | - Vincent Joris
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurosurgery Unit, Brussels, Belgium
| | - Christian Raftopoulos
- Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurosurgery Unit, Brussels, Belgium
| | - Thierry Duprez
- Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Medical Imaging Department, Neuroradiology Unit, Belgium
| | - Susana Ferrao Santos
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurology Unit, Brussels, Belgium
| | - Pieter van Mierlo
- Medical Image and Signal Processing, Ghent University, Ghent, Belgium; Epilog NV, Ghent, Belgium
| | - Riëm El Tahry
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurology Unit, Brussels, Belgium; WELBIO Department, WEL Research Institute, Avenue Pasteur 6, 1300 Wavre, Belgium
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Spinelli L, Baroumand AG, Vulliemoz S, Momjian S, Strobbe G, van Mierlo P, Seeck M. Semiautomatic interictal electric source localization based on long-term electroencephalographic monitoring: A prospective study. Epilepsia 2022; 64:951-961. [PMID: 36346269 DOI: 10.1111/epi.17460] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Electric source imaging (ESI) of interictal epileptiform discharges (IEDs) has shown significant yield in numerous studies; however, its implementation at most centers is labor- and cost-intensive. Semiautomatic ESI analysis (SAEA) has been proposed as an alternative and has previously shown benefit. Computer-assisted automatic spike cluster retrieval, averaging, and source localization are carried out for each cluster and are then reviewed by an expert neurophysiologist, to determine their relevance for the individual case. Here, we examine its yield in a prospective single center study. METHOD Between 2017 and 2022, 122 patients underwent SAEA. Inclusion criteria for the current study were unifocal epilepsy disorder, epilepsy surgery with curative purpose, and postoperative follow-up of 2 years or more. All patients (N=40) had continuous video-electroencephalographic (EEG) monitoring with 37 scalp electrodes, which underwent SAEA. Forty patients matched our inclusion criteria. RESULTS Twenty patients required intracranial monitoring; 13 were magnetic resonance imaging (MRI)-negative. Mean duration of analyzed EEG was 4.3 days (±3.1 days), containing a mean of 12 749 detected IEDs (±22 324). The sensitivity, specificity, and accuracy of SAEA for localizing the epileptogenic focus of the entire group were 74.3%, 80%, and 75%, respectively, leading to an odds ratio (OR) of 11.5 to become seizure-free if the source was included in the resection volume (p < .05). In patients with extratemporal lobe epilepsy, our results indicated an accuracy of 68% (OR=11.7). For MRI-negative patients (n = 13) and patients requiring intracranial EEG (n = 20), we found a similarly high accuracy of 84.6% (OR=19) and 75% (OR = 15.9), respectively. SIGNIFICANCE In this prospective study of SAEA of long-term video-EEG, spanning several days, we found excellent localizing information and a high yield, even in difficult patient groups. This compares favorably to high-density ESI, most likely due to marked improved signal-to-noise ratio of the averaged IEDs. We propose including ESI, or SAEA, in the workup of all patients who are referred for epilepsy surgery.
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Affiliation(s)
- Laurent Spinelli
- EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
| | - Amir G Baroumand
- Medical Image and Signal Processing, Ghent University, Ghent, Belgium.,Epilog, Ghent, Belgium
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
| | - Shahan Momjian
- EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
| | | | - Pieter van Mierlo
- Medical Image and Signal Processing, Ghent University, Ghent, Belgium.,Epilog, Ghent, Belgium
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
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9
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Liu AA, Henin S, Abbaspoor S, Bragin A, Buffalo EA, Farrell JS, Foster DJ, Frank LM, Gedankien T, Gotman J, Guidera JA, Hoffman KL, Jacobs J, Kahana MJ, Li L, Liao Z, Lin JJ, Losonczy A, Malach R, van der Meer MA, McClain K, McNaughton BL, Norman Y, Navas-Olive A, de la Prida LM, Rueckemann JW, Sakon JJ, Skelin I, Soltesz I, Staresina BP, Weiss SA, Wilson MA, Zaghloul KA, Zugaro M, Buzsáki G. A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations. Nat Commun 2022; 13:6000. [PMID: 36224194 PMCID: PMC9556539 DOI: 10.1038/s41467-022-33536-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 09/21/2022] [Indexed: 02/05/2023] Open
Abstract
Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.
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Affiliation(s)
- Anli A Liu
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA
| | - Simon Henin
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Saman Abbaspoor
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Anatol Bragin
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Elizabeth A Buffalo
- Department of Physiology and Biophysics, Washington National Primate Center, University of Washington, Seattle, WA, USA
| | - Jordan S Farrell
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - David J Foster
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Tamara Gedankien
- Department of Biomedical Engineering, Department of Neurological Surgery, Columbia University, New York, NY, USA
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jennifer A Guidera
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program, Department of Bioengineering, University of California, San Francisco, San Francisco, CA, USA
| | - Kari L Hoffman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Joshua Jacobs
- Department of Biomedical Engineering, Department of Neurological Surgery, Columbia University, New York, NY, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lin Li
- Department of Biomedical Engineering, University of North Texas, Denton, TX, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - Jack J Lin
- Department of Neurology, Center for Mind and Brain, University of California Davis, Oakland, CA, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - Rafael Malach
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | | | - Kathryn McClain
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA
| | - Bruce L McNaughton
- The Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada
| | - Yitzhak Norman
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | | | | | - Jon W Rueckemann
- Department of Physiology and Biophysics, Washington National Primate Center, University of Washington, Seattle, WA, USA
| | - John J Sakon
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ivan Skelin
- Department of Neurology, Center for Mind and Brain, University of California Davis, Oakland, CA, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Bernhard P Staresina
- Department of Experimental Psychology, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Shennan A Weiss
- Brookdale Hospital Medical Center, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences and Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, USA
| | - Michaël Zugaro
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
| | - György Buzsáki
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA.
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA.
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10
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Singh J, Ebersole JS, Brinkmann BH. From theory to practical fundamentals of electroencephalographic source imaging in localizing the epileptogenic zone. Epilepsia 2022; 63:2476-2490. [PMID: 35811476 PMCID: PMC9796417 DOI: 10.1111/epi.17361] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/07/2022] [Accepted: 07/07/2022] [Indexed: 01/01/2023]
Abstract
With continued advancement in computational technologies, the analysis of electroencephalography (EEG) has shifted from pure visual analysis to a noninvasive computational technique called EEG source imaging (ESI), which involves mathematical modeling of dipolar and distributed sources of a given scalp EEG pattern. ESI is a noninvasive phase I test for presurgical localization of the seizure onset zone in focal epilepsy. It is a relatively inexpensive modality, as it leverages scalp EEG and magnetic resonance imaging (MRI) data already collected typically during presurgical evaluation. With an adequate number of electrodes and combined with patient-specific MRI-based head models, ESI has proven to be a valuable and accurate clinical diagnostic tool for localizing the epileptogenic zone. Despite its advantages, however, ESI is routinely used at only a minority of epilepsy centers. This paper reviews the current evidence and practical fundamentals for using ESI of interictal and ictal epileptic activity during the presurgical evaluation of drug-resistant patients. We identify common errors in processing and interpreting ESI studies, describe the differences in approach needed for localizing interictal and ictal EEG discharges through practical examples, and describe best practices for optimizing the diagnostic information available from these studies.
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Affiliation(s)
- Jaysingh Singh
- Department of NeurologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - John S. Ebersole
- Northeast Regional Epilepsy GroupAtlantic Health Neuroscience InstituteSummitNew JerseyUSA
| | - Benjamin H. Brinkmann
- Department of NeurologyMayo ClinicRochesterMinnesotaUSA,Department of Biomedical EngineeringMayo ClinicRochesterMinnesotaUSA
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11
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Cheng C, Liu Y, You B, Zhou Y, Gao F, Yang L, Dai Y. Multilevel Feature Learning Method for Accurate Interictal Epileptiform Spike Detection. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2506-2516. [PMID: 35877795 DOI: 10.1109/tnsre.2022.3193666] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Interictal epileptiform spike (referred to as spike) detected from electroencephalograms lasting only 20- to 200-ms can provide a reliable evidence-based indicator for clinical seizure type diagnosis. Recent feature representation approaches focus either on the concrete-level or on abstract-level information mining of the spike, thus demonstrating suboptimal detection performance. Additionally, existing abstract-level information mining methods of the spike based deep learning networks have not realized the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity, which affects detection performance. Thus, a multilevel feature learning method for accurate spike detection was proposed in this study. Specifically, the spatio-temporal-frequency multidomain information in concrete-level first are inferred the common mimetic properties of the spike using the multidomain feature extractors. Then, the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity is suitably captured using the temporal convolutional network. Finally, the spatio-temporal-frequency multidomain long-term dependent feature representation of spike is calculated using the element-wise manner to fuse the feature representation in concrete- and abstract-levels. The experimental results indicate that the proposed method can achieve an accuracy of 90.62±1.38%, sensitivity of 90.38±1.52%, specificity of 91.00±1.60%, precision of 90.33±4.71%, and the false detection rate per minute is 0.148±0.020m-1, which are higher than when using the feature representation in the concrete- or abstract-level alone. Additionally, the detection results indicate that the proposed method avoids the subjectivity and inefficiency of visual inspection, and it enables a highly accurate detection of the spike.
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