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Hays MA, Daraie AH, Smith RJ, Sarma SV, Crone NE, Kang JY. Network excitability of stimulation-induced spectral responses helps localize the seizure onset zone. Clin Neurophysiol 2024; 166:43-55. [PMID: 39096821 PMCID: PMC11401764 DOI: 10.1016/j.clinph.2024.07.010] [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: 10/09/2023] [Revised: 03/11/2024] [Accepted: 07/19/2024] [Indexed: 08/05/2024]
Abstract
OBJECTIVE While evoked potentials elicited by single pulse electrical stimulation (SPES) may assist seizure onset zone (SOZ) localization during intracranial EEG (iEEG) monitoring, induced high frequency activity has also shown promising utility. We aimed to predict SOZ sites using induced cortico-cortical spectral responses (CCSRs) as an index of excitability within epileptogenic networks. METHODS SPES was conducted in 27 epilepsy patients undergoing iEEG monitoring and CCSRs were quantified by significant early (10-200 ms) increases in power from 10 to 250 Hz. Using response power as CCSR network connection strengths, graph centrality measures (metrics quantifying each site's influence within the network) were used to predict whether sites were within the SOZ. RESULTS Across patients with successful surgical outcomes, greater CCSR centrality predicted SOZ sites and SOZ sites targeted for surgical treatment with median AUCs of 0.85 and 0.91, respectively. We found that the alignment between predicted and targeted SOZ sites predicted surgical outcome with an AUC of 0.79. CONCLUSIONS These findings indicate that network analysis of CCSRs can be used to identify increased excitability of SOZ sites and discriminate important surgical targets within the SOZ. SIGNIFICANCE CCSRs may supplement traditional passive iEEG monitoring in seizure localization, potentially reducing the need for recording numerous seizures.
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Affiliation(s)
- Mark A Hays
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Amir H Daraie
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA; Department of Neuroengineering, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Joon Y Kang
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
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2
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Zhai SR, Sarma SV, Gunnarsdottir K, Crone NE, Rouse AG, Cheng JJ, Kinsman MJ, Landazuri P, Uysal U, Ulloa CM, Cameron N, Inati S, Zaghloul KA, Boerwinkle VL, Wyckoff S, Barot N, González-Martínez JA, Kang JY, Smith RJ. Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1425625. [PMID: 39229346 PMCID: PMC11368849 DOI: 10.3389/fnetp.2024.1425625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/24/2024] [Indexed: 09/05/2024]
Abstract
Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.
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Affiliation(s)
- Sophia R. Zhai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Sridevi V. Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Kristin Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Nathan E. Crone
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Adam G. Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Jennifer J. Cheng
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Michael J. Kinsman
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Patrick Landazuri
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Utku Uysal
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States
| | - Carol M. Ulloa
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States
| | - Nathaniel Cameron
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Sara Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Kareem A. Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Varina L. Boerwinkle
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ, United States
| | - Sarah Wyckoff
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ, United States
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Joon Y. Kang
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Rachel June Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, United States
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Yang B, Zhao B, Li C, Mo J, Guo Z, Li Z, Yao Y, Fan X, Cai D, Sang L, Zheng Z, Gao D, Zhao X, Wang X, Zhang C, Hu W, Shao X, Zhang J, Zhang K. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy. Clin Neurophysiol 2024; 158:103-113. [PMID: 38218076 DOI: 10.1016/j.clinph.2023.12.135] [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: 08/08/2023] [Revised: 12/03/2023] [Accepted: 12/19/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE We aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes. METHODS Fifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area. RESULTS The area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036). CONCLUSIONS This new method can be used to predict the SOZ localization in various focal epilepsy phenotypes. SIGNIFICANCE This study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.
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Affiliation(s)
- Bowen Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chao Li
- Department of Radiology, Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhihao Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zilin Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuan Yao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiuliang Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Du Cai
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Zhong Zheng
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Dongmei Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuemin Zhao
- Department of Neurophysiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
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Ramantani G, Westover MB, Gliske S, Sarnthein J, Sarma S, Wang Y, Baud MO, Stacey WC, Conrad EC. Passive and active markers of cortical excitability in epilepsy. Epilepsia 2023; 64 Suppl 3:S25-S36. [PMID: 36897228 PMCID: PMC10512778 DOI: 10.1111/epi.17578] [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: 02/01/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.
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Affiliation(s)
- Georgia Ramantani
- Department of Neuropediatrics and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Data Science, Massachusetts General Hospital McCance Center for Brain Health, Boston, Massachusetts, USA
- Research Affiliate Faculty, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Research Affiliate Faculty, Broad Institute, Cambridge, Massachusetts, USA
| | - Stephen Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sridevi Sarma
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - William C Stacey
- Department of Neurology, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Division of Neurology, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Frauscher B, Bartolomei F, Baud MO, Smith RJ, Worrell G, Lundstrom BN. Stimulation to probe, excite, and inhibit the epileptic brain. Epilepsia 2023; 64 Suppl 3:S49-S61. [PMID: 37194746 PMCID: PMC10654261 DOI: 10.1111/epi.17640] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/18/2023]
Abstract
Direct cortical stimulation has been applied in epilepsy for nearly a century and has experienced a renaissance, given unprecedented opportunities to probe, excite, and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug-resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, and is further complicated by epilepsy being characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 Conference (International Conference on Technology and Analysis for Seizures), we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on the usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimizing use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long-term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive electroencephalographic recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker-informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities.
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Affiliation(s)
- Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Fabrice Bartolomei
- Institut de Neurosciences des Systèmes, Aix Marseille University, Marseille, France. AP-HM, Service de Neurophysiologie Clinique, Hôpital de la Timone, Marseille, France
| | - Maxime O. Baud
- Sleep-Wake-Epilepsy Center, NeuroTec and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern
| | - Rachel J. Smith
- University of Alabama at Birmingham, Electrical and Computer Engineering Department, Birmingham, Alabama, US. University of Alabama at Birmingham, Neuroengineering Program, Birmingham, Alabama, US
| | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, US
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Gallagher RS, Sinha N, Pattnaik AR, Ojemann WK, Lucas A, LaRocque JJ, Bernabei JM, Greenblatt AS, Sweeney EM, Chen HI, Davis KA, Conrad EC, Litt B. Quantifying interictal intracranial EEG to predict focal epilepsy. ARXIV 2023:arXiv:2307.15170v1. [PMID: 37547655 PMCID: PMC10402195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Introduction Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. Methods We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the "5 Sense Score (5SS)," a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians' choice of therapeutic intervention and the patient outcome. Results The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Conclusions Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.
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Affiliation(s)
- Ryan S Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Akash R. Pattnaik
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - William K.S. Ojemann
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Joshua J. LaRocque
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - John M Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | | | - Elizabeth M Sweeney
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - H Isaac Chen
- Department of Neurosurgery, University of Pennsylvania
- Corporal Michael J. Crescenz Veterans Affairs Medical Center
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
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Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023; 146:2248-2258. [PMID: 36623936 PMCID: PMC10232272 DOI: 10.1093/brain/awad007] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Li
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Andrew Y Revell
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kristin M Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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