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Bagić AI, Bowyer SM, Burgess RC, Funke ME, Lowden A, Mohamed IS, Wilson T, Zhang W, Zillgitt AJ, Tenney JR. Role of optically pumped magnetometers in presurgical epilepsy evaluation: Commentary of the American Clinical Magnetoencephalography Society. Epilepsia 2023; 64:3155-3159. [PMID: 37728519 DOI: 10.1111/epi.17770] [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: 06/09/2023] [Revised: 08/18/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023]
Abstract
One of the major challenges of modern epileptology is the underutilization of epilepsy surgery for treatment of patients with focal, medication resistant epilepsy (MRE). Aggravating this distressing failure to deliver optimum care to these patients is the underuse of proven localizing tools, such as magnetoencephalography (MEG), a clinically validated, non-invasive, neurophysiological method used to directly measure and localize brain activity. A sizable mass of published evidence indicates that MEG can improve identification of surgical candidates and guide pre-surgical planning, increasing the yield of SEEG and improving operative outcomes. However, despite at least 10 common, evidence supported, clinical scenarios in MRE patients where MEG can offer non-redundant information and improve the pre-surgical evaluation, it is regularly used by only a minority of USA epilepsy centers. The current state of the art in MEG sensors employs SQUIDs, which require cooling with liquid helium to achieve superconductivity. This sensor technology has undergone significant generational improvement since whole head MEG scanners were introduced around in 1990s, but still has limitations. Further advances in sensor technology which may make ME G more easily accessible and affordable have been eagerly awaited, and development of new techniques should be encouraged. Of late, optically pumped magnetometers (OPMs) have received considerable attention, even prompting some potential acquisitions of new MEG systems to be put on hold, based on a hope that OPMs will usher in a new generation of MEG equipment and procedures. The development of any new clinical test used to guide intracranial EEG monitoring and/or surgical planning must address several specific issues. The goal of this commentary is to recognize the current state of OPM technology and to suggest a framework for it to advance in the clinical realm where it can eventually be deemed clinically valuable to physicians and patients. The American Clinical MEG Society (ACMEGS) strongly supports more advanced and less expensive technology and looks forward to continuing work with researchers to develop new sensors and clinical devices which will improve the experience and outcome for patients, and perhaps extend the role of MEG. However, currently, there are no OPM devices ready for practical clinical use. Based on the engineering obstacles and the clinical tradeoffs to be resolved, the assessment of experts suggests that there will most likely be another decade relying solely on "frozen SQUIDs" in the clinical MEG field.
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Affiliation(s)
- Anto I Bagić
- University of Pittsburgh Comprehensive Epilepsy Center, Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Susan M Bowyer
- MEG Laboratory, Henry Ford Hospital, Wayne State University, Detroit, Michigan, USA
| | - Richard C Burgess
- Magnetoencephalography Laboratory, Cleveland Clinic Epilepsy Center, Cleveland, Ohio, USA
| | - Michael E Funke
- Department of Pediatrics, University of Texas Health Science Center, McGovern Medical School, Houston, Texas, USA
| | - Andrea Lowden
- Division of Pediatric Neurology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Ismail S Mohamed
- Department of Pediatrics, University of Alabama, Birmingham, Alabama, USA
| | - Tony Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, Nebraska, USA
| | - Wenbo Zhang
- Minnesota Epilepsy Group, Roseville, Minnesota, USA
| | - Andrew J Zillgitt
- Corewell Health William Beaumont University Hospital, Royal Oak, Minnesota, USA
| | - Jeffrey R Tenney
- MEG Center, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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2
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Sun R, Zhang W, Bagić A, He B. Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes. Neuroimage 2023; 281:120366. [PMID: 37716593 PMCID: PMC10771628 DOI: 10.1016/j.neuroimage.2023.120366] [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: 01/27/2023] [Revised: 08/07/2023] [Accepted: 09/06/2023] [Indexed: 09/18/2023] Open
Abstract
Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and thus the need of modeling the underlying brain dynamics for regularizations. Advances in generative models provide opportunities for more accurate and realistic source modeling that could offer an alternative approach to ESI for modeling the underlying brain dynamics beyond equivalent physical source models. However, it is not straightforward to explicitly formulate the knowledge arising from these generative models within the conventional ESI framework. Here we investigate a novel source imaging framework based on mesoscale neuronal modeling and deep learning (DL) that can learn the sensor-source mapping relationship directly from MEG data for ESI. Two DL-based ESI models were trained based on data generated by neural mass models and either generic or personalized head models. The robustness of the two DL models was evaluated by systematic computer simulations and clinical validation in a cohort of 29 drug-resistant focal epilepsy patients who underwent intracranial EEG (iEEG) evaluation or surgical resection. Results estimated from pre-operative MEG interictal spikes were quantified using the overlap with resection regions and the distance to the seizure-onset zone (SOZ) defined by iEEG recordings. The DL-based ESI provided robust results when no personalized head geometry is considered, reaching a spatial dispersion of 21.90 ± 19.03 mm, sublobar concordance of 83 %, and sublobar sensitivity and specificity of 66 and 97 % respectively. When using personalized head geometry derived from individual patients' MRI in the training data, personalized DL-based ESI model can further improve the performance and reached a spatial dispersion of 8.19 ± 8.14 mm, sublobar concordance of 93 %, and sublobar sensitivity and specificity of 77 and 99 % respectively. When compared to the SOZ, the localization error of the personalized approach is 15.78 ± 5.54 mm, outperforming the conventional benchmarks. This work demonstrates that combining generative models and deep learning enables an accurate and robust imaging of epileptogenic zone from MEG recordings with strong sublobar precision, suggesting its added value to enhancing MEG source localization and imaging, and to epilepsy source localization and other clinical applications.
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Affiliation(s)
- Rui Sun
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Wenbo Zhang
- Minnesota Epilepsy Group, John Nasseff Neuroscience Center at United Hospital, Saint Paul, USA
| | - Anto Bagić
- Department of Neurology, University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, Pittsburgh, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
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3
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Zheng L, Liao P, Wu X, Cao M, Cui W, Lu L, Xu H, Zhu L, Lyu B, Wang X, Teng P, Wang J, Vogrin S, Plummer C, Luan G, Gao JH. An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography. J Neural Eng 2023; 20:046036. [PMID: 37615416 DOI: 10.1088/1741-2552/acef92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023]
Abstract
Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.
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Affiliation(s)
- Li Zheng
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Pan Liao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Xiuwen Wu
- Changping Laboratory, Beijing, People's Republic of China
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Miao Cao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Wei Cui
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Lingxi Lu
- Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, People's Republic of China
| | - Hui Xu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Linlin Zhu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Bingjiang Lyu
- Changping Laboratory, Beijing, People's Republic of China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Pengfei Teng
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jing Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Simon Vogrin
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Chris Plummer
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Jia-Hong Gao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
- McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China
- National Biomedical Imaging Center, Peking University, Beijing, People's Republic of China
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Laohathai C, Funke M. Epilepsy highlight: Ictal MEG in epilepsy surgery candidates - Results from largest cohort. Clin Neurophysiol 2023; 145:98-99. [PMID: 36435692 DOI: 10.1016/j.clinph.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022]
Affiliation(s)
| | - Michael Funke
- Department of Pediatrics, University of Texas Health Science Center, McGovern Medical School, Houston, TX, USA
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Maldjian JA, Lee R, Jordan J, Davenport EM, Proskovec AL, Wintermark M, Stufflebeam S, Anderson J, Mukherjee P, Nagarajan SS, Ferrari P, Gaetz W, Schwartz E, Roberts TPL. ACR White Paper on Magnetoencephalography and Magnetic Source Imaging: A Report from the ACR Commission on Neuroradiology. AJNR Am J Neuroradiol 2022; 43:E46-E53. [PMID: 36456085 DOI: 10.3174/ajnr.a7714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 12/04/2022]
Abstract
Magnetoencephalography, the extracranial detection of tiny magnetic fields emanating from intracranial electrical activity of neurons, and its source modeling relation, magnetic source imaging, represent a powerful functional neuroimaging technique, able to detect and localize both spontaneous and evoked activity of the brain in health and disease. Recent years have seen an increased utilization of this technique for both clinical practice and research, in the United States and worldwide. This report summarizes current thinking, presents recommendations for clinical implementation, and offers an outlook for emerging new clinical indications.
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Affiliation(s)
- J A Maldjian
- From the Advanced Neuroscience Imaging Research Laboratory (J.A.M., E.M.D., A.L.P.) .,MEG Center of Excellence (J.A.M., E.M.D., A.L.P.).,Department of Radiology (J.A.M., E.M.D., A.L.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - R Lee
- Department of Neuroradiology (R.L.), University of California San Diego, San Diego, California
| | - J Jordan
- ACR Commission on Neuroradiology (J.J.), American College of Radiology, Reston, Virginia.,Stanford University School of Medicine (J.J.), Stanford, California
| | - E M Davenport
- From the Advanced Neuroscience Imaging Research Laboratory (J.A.M., E.M.D., A.L.P.).,MEG Center of Excellence (J.A.M., E.M.D., A.L.P.).,Department of Radiology (J.A.M., E.M.D., A.L.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - A L Proskovec
- From the Advanced Neuroscience Imaging Research Laboratory (J.A.M., E.M.D., A.L.P.).,MEG Center of Excellence (J.A.M., E.M.D., A.L.P.).,Department of Radiology (J.A.M., E.M.D., A.L.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - M Wintermark
- Department of Neuroradiology (M.W.), University of Texas MD Anderson Center, Houston, Texas
| | - S Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging (S.S.), Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - J Anderson
- Department of Radiology and Imaging Sciences (J.A.), University of Utah School of Medicine, Salt Lake City, Utah
| | - P Mukherjee
- Department of Radiology and Biomedical Imaging (P.M., S.S.N.), University of California, San Francisco, San Francisco, California
| | - S S Nagarajan
- Department of Radiology and Biomedical Imaging (P.M., S.S.N.), University of California, San Francisco, San Francisco, California
| | - P Ferrari
- Pediatric Neurosciences (P.F.), Helen DeVos Children's Hospital, Grand Rapids, Michigan.,Department of Pediatrics and Human Development (P.F.), College of Human Medicine, Michigan State University, Grand Rapids, Michigan
| | - W Gaetz
- Department of Radiology (W.G., E.S., T.P.L.R.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - E Schwartz
- Department of Radiology (W.G., E.S., T.P.L.R.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - T P L Roberts
- Department of Radiology (W.G., E.S., T.P.L.R.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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6
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Alkawadri R, Enatsu R, Hämäläinen M, Bagić A. Editorial: Magnetoencephalography: Methodological innovation paves the way for scientific discoveries and new clinical applications. Front Neurol 2022; 13:1056301. [PMID: 36504656 PMCID: PMC9731220 DOI: 10.3389/fneur.2022.1056301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/13/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Rafeed Alkawadri
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, United States,*Correspondence: Rafeed Alkawadri ; https://www.humanbrainmapping.net/contactus
| | - Rei Enatsu
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Matti Hämäläinen
- Department of Radiology, Harvard Medical School, Boston, MA, United States,Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Anto Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, United States
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Nandakumar N, Hsu D, Ahmed R, Venkataraman A. DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity. IEEE Trans Biomed Eng 2022; 70:216-227. [PMID: 35776823 PMCID: PMC9841829 DOI: 10.1109/tbme.2022.3187942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data. METHODS Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds. RESULTS We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort. CONCLUSION Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy. SIGNIFICANCE While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.
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Affiliation(s)
- Naresh Nandakumar
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - David Hsu
- Department of Neurology, University of Wisconsin, USA
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
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8
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Malik S. Magnetoencephalography: at the forefront of optimizing epilepsy surgery. FUTURE NEUROLOGY 2022. [DOI: 10.2217/fnl-2022-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Saleem Malik
- Medical Director, Comprehensive Epilepsy Care program; Director, MEG, TMS lab; Director, IntraOp Neuro monitoring; Jane & Justin Neuroscience Center, Cook Children’s Health Care System, 1500 Cooper Street, 4th Floor, Fort Worth, TX 76104-2734, USA
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9
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Spooner RK, Madhavan D, Aizenberg MR, Wilson TW. Retrospective comparison of motor and somatosensory MEG mapping-Considerations for better clinical applications. Neuroimage Clin 2022; 35:103045. [PMID: 35597033 PMCID: PMC9123261 DOI: 10.1016/j.nicl.2022.103045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/29/2022] [Accepted: 05/09/2022] [Indexed: 11/17/2022]
Abstract
MEG is a clinically validated tool for presurgical functional mapping. The success rate for MEG somatosensory and motor mapping is not fully known. Comprehensive mapping protocols increase the accuracy of sensorimotor mapping. Major sources of mapping failures include low SNR, magnetic artifacts, and motion. Recommendations for improving mapping success rates in the future are discussed.
While magnetoencephalography (MEG) has proven to be a valuable and reliable tool for presurgical functional mapping of eloquent cortices for at least two decades, widespread use of this technique by clinicians has remained elusive. This modest application may be attributable, at least in part, to misunderstandings regarding the success rate of such mapping procedures, as well as the primary sources contributing to mapping failures. To address this, we conducted a retrospective comparison of sensorimotor functional mapping success rates in 141 patients with epilepsy and 75 tumor patients from the Center for MEG in Omaha, NE. Neurosurgical candidates either completed motor mapping (i.e., finger tapping paradigm), somatosensory mapping (i.e., peripheral stimulation paradigm), or both motor and somatosensory protocols during MEG. All MEG data underwent subsequent time-domain averaging and source localization of left and right primary motor (M1) and somatosensory (S1) cortices was conducted using a single equivalent dipole model. Successful mapping was determined based on dipole goodness of fit metrics ∼ 95%, as well as an accurate and conceivable spatial correspondence to precentral and postcentral gyri for M1 and S1, respectively. Our results suggest that mapping M1 in epilepsy and tumor patients was on average 94.5% successful, when patients only completed motor mapping protocols. In contrast, mapping S1 was successful 45–100% of the time in these patient groups when they only completed somatosensory mapping paradigms. Importantly, Z-tests for independent proportions revealed that the percentage of successful S1 mappings significantly increased to ∼ 94% in epilepsy patients who completed both motor/somatosensory mapping protocols during MEG. Together, these data suggest that ordering more comprehensive mapping procedures (e.g., both motor and somatosensory protocols for a collective sensorimotor network) may substantially increase the accuracy of presurgical functional mapping by providing more extensive data from which to base interpretations. Moreover, clinicians and magnetoencephalographers should be considerate of the major contributors to mapping failures (i.e., low SNR, excessive motion and magnetic artifacts) in order to further increase the percentage of cases achieving successful mapping of eloquent cortices.
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Affiliation(s)
- Rachel K Spooner
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University, Düsseldorf, Germany.
| | - Deepak Madhavan
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
| | | | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
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HEGAZY M, GAVVALA J. Magnetoencephalography in clinical practice. ARQUIVOS DE NEURO-PSIQUIATRIA 2022; 80:523-529. [DOI: 10.1590/0004-282x-anp-2021-0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/14/2021] [Indexed: 11/22/2022]
Abstract
ABSTRACT Magnetoencephalography (MEG) is a neurophysiological technique that measures the magnetic fields associated with neuronal activity in the brain. It is closely related but distinct from its counterpart electroencephalography (EEG). The first MEG was recorded more than 50 years ago and has technologically evolved over this time. It is now well established in clinical practice particularly in the field of epilepsy surgery and functional brain mapping. However, underutilization and misunderstanding of the clinical applications of MEG is a challenge to more widespread use of this technology. A fundamental understanding of the neurophysiology and physics of MEG is discussed in this article as well as practical issues related to implementation, analysis, and clinical applications. The future of MEG and some potential clinical applications are briefly reviewed.
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11
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Cao M, Vogrin SJ, Peterson ADH, Woods W, Cook MJ, Plummer C. Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach. Front Neurol 2022; 13:837893. [PMID: 35422755 PMCID: PMC9001937 DOI: 10.3389/fneur.2022.837893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies—limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.
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Affiliation(s)
- Miao Cao
- Center for MRI Research, Peking University, Beijing, China
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Simon J. Vogrin
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Andre D. H. Peterson
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - William Woods
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Mark J. Cook
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Chris Plummer
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
- *Correspondence: Chris Plummer
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Poghosyan V, Rampp S, Wang ZI. Editorial: Magnetoencephalography (MEG) in Epilepsy and Neurosurgery. Front Hum Neurosci 2022; 16:873153. [PMID: 35360284 PMCID: PMC8963912 DOI: 10.3389/fnhum.2022.873153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Vahe Poghosyan
- Department of Neurophysiology, National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia
- *Correspondence: Vahe Poghosyan
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany
- Department of Neurosurgery, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Zhong Irene Wang
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
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13
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Jiang X, Ye S, Sohrabpour A, Bagić A, He B. Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements. Neuroimage Clin 2021; 33:102903. [PMID: 34864288 PMCID: PMC8648830 DOI: 10.1016/j.nicl.2021.102903] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/26/2021] [Accepted: 11/27/2021] [Indexed: 11/23/2022]
Abstract
Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients' interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.
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Affiliation(s)
- Xiyuan Jiang
- Department of Biomedical Engineering, Carnegie Mellon University, USA
| | - Shuai Ye
- Department of Biomedical Engineering, Carnegie Mellon University, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, Carnegie Mellon University, USA
| | - Anto Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, USA.
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14
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Laohathai C, Ebersole JS, Mosher JC, Bagić AI, Sumida A, Von Allmen G, Funke ME. Practical Fundamentals of Clinical MEG Interpretation in Epilepsy. Front Neurol 2021; 12:722986. [PMID: 34721261 PMCID: PMC8551575 DOI: 10.3389/fneur.2021.722986] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/06/2021] [Indexed: 11/29/2022] Open
Abstract
Magnetoencephalography (MEG) is a neurophysiologic test that offers a functional localization of epileptic sources in patients considered for epilepsy surgery. The understanding of clinical MEG concepts, and the interpretation of these clinical studies, are very involving processes that demand both clinical and procedural expertise. One of the major obstacles in acquiring necessary proficiency is the scarcity of fundamental clinical literature. To fill this knowledge gap, this review aims to explain the basic practical concepts of clinical MEG relevant to epilepsy with an emphasis on single equivalent dipole (sECD), which is one the most clinically validated and ubiquitously used source localization method, and illustrate and explain the regional topology and source dynamics relevant for clinical interpretation of MEG-EEG.
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Affiliation(s)
- Christopher Laohathai
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
- Department of Neurology, Saint Louis University, Saint Louis, MO, United States
| | - John S. Ebersole
- Northeast Regional Epilepsy Group, Atlantic Health Neuroscience Institute, Summit, NJ, United States
| | - John C. Mosher
- Department of Neurology, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Anto I. Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, University of Pittsburgh Medical Center, Pittsburg, PA, United States
| | - Ai Sumida
- Department of Neurology, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Gretchen Von Allmen
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Michael E. Funke
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
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15
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Tripathi M, Kaur K, Ramanujam B, Viswanathan V, Bharti K, Singh G, Singh V, Garg A, Bal CS, Tripathi M, Sharma MC, Pandey R, Dash D, Mandal P, Chandra PS. Diagnostic added value of interictal magnetic source imaging in presurgical evaluation of persons with epilepsy: A prospective blinded study. Eur J Neurol 2021; 28:2940-2951. [PMID: 34124810 DOI: 10.1111/ene.14935] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/27/2021] [Accepted: 05/06/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE In presurgical evaluation for epilepsy surgery, information is sourced from various imaging modalities to accurately localize the epileptogenic zone. Magnetoencephalography (MEG) is a newer noninvasive technique for localization. However, there is limited literature to evaluate if MEG provides additional advantage over the conventional imaging modalities in clinical decision making. The objective of this study was to assess the diagnostic added value of MEG in decision making before epilepsy surgery. METHOD This was a prospective observational study. Patients underwent 3 h of recording in a MEG scanner, and the resulting localizations were compared with other complimentary investigations. Added value of MEG (considered separately from high-density electroencephalography) was defined as the frequency of cases in which (i) the information provided by magnetic source imaging (MSI) avoided implantation of intracranial electrodes and the patient was directly cleared for surgery, and (ii) MSI indicated additional substrates for implantation of intracranial electrodes. Postoperative seizure freedom was used as the diagnostic reference by which to measure the localizing accuracy of MSI. RESULTS A total of 102 patients underwent epilepsy surgery. MEG provided nonredundant information, which contributed to deciding the course of surgery in 33% of the patients, and prevented intracranial recordings in 19%. A total of 76% of the patients underwent surgical resection in sublobes concordant with MSI localization, and the diagnostic odds ratio for good (Engel I) outcome in these patients was 2.3 (95% confidence interval 0.68, 7.86; p = 0.183) after long-term follow-up of 36 months. CONCLUSION Magnetic source imaging yields additional useful information which can significantly alter as well as improve the surgical strategy for persons with epilepsy.
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Affiliation(s)
- Manjari Tripathi
- Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Kirandeep Kaur
- Neurology, All India Institute of Medical Sciences, New Delhi, India.,MEG Facility, National Brain Research Institute, Manesar, India
| | | | - Vibhin Viswanathan
- Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.,MEG Resource Facility, Collaborative Project Between AIIMS & NBRC, National Brain Research Center, Manesar, India
| | - Kamal Bharti
- MEG Resource Facility, Collaborative Project Between AIIMS & NBRC, National Brain Research Center, Manesar, India
| | - Gaurav Singh
- MEG Resource Facility, Collaborative Project Between AIIMS & NBRC, National Brain Research Center, Manesar, India
| | - Vivek Singh
- MEG Resource Facility, Collaborative Project Between AIIMS & NBRC, National Brain Research Center, Manesar, India
| | - Ajay Garg
- Neuroradiology, All India Institute of Medical Sciences, New Delhi, India
| | - Chandra Sekhar Bal
- Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Madhavi Tripathi
- Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
| | | | - Ravindra Pandey
- Biostatistics, All India Institute of Medical Sciences, New Delhi, India
| | - Deepa Dash
- Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Pravat Mandal
- MEG Resource Facility, Collaborative Project Between AIIMS & NBRC, National Brain Research Center, Manesar, India
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16
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Watkins MW, Shah EG, Funke ME, Garcia-Tarodo S, Shah MN, Tandon N, Maestu F, Laohathai C, Sandberg DI, Lankford J, Thompson S, Mosher J, Von Allmen G. Indications for Inpatient Magnetoencephalography in Children - An Institution's Experience. Front Hum Neurosci 2021; 15:667777. [PMID: 34149382 PMCID: PMC8213217 DOI: 10.3389/fnhum.2021.667777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 04/14/2021] [Indexed: 11/13/2022] Open
Abstract
Magnetoencephalography (MEG) is recognized as a valuable non-invasive clinical method for localization of the epileptogenic zone and critical functional areas, as part of a pre-surgical evaluation for patients with pharmaco-resistant epilepsy. MEG is also useful in localizing functional areas as part of pre-surgical planning for tumor resection. MEG is usually performed in an outpatient setting, as one part of an evaluation that can include a variety of other testing modalities including 3-Tesla MRI and inpatient video-electroencephalography monitoring. In some clinical circumstances, however, completion of the MEG as an inpatient can provide crucial ictal or interictal localization data during an ongoing inpatient evaluation, in order to expedite medical or surgical planning. Despite well-established clinical indications for performing MEG in general, there are no current reports that discuss indications or considerations for completion of MEG on an inpatient basis. We conducted a retrospective institutional review of all pediatric MEGs performed between January 2012 and December 2020, and identified 34 cases where MEG was completed as an inpatient. We then reviewed all relevant medical records to determine clinical history, all associated diagnostic procedures, and subsequent treatment plans including epilepsy surgery and post-surgical outcomes. In doing so, we were able to identify five indications for completing the MEG on an inpatient basis: (1) super-refractory status epilepticus (SRSE), (2) intractable epilepsy with frequent electroclinical seizures, and/or frequent or repeated episodes of status epilepticus, (3) intractable epilepsy with infrequent epileptiform discharges on EEG or outpatient MEG, or other special circumstances necessitating inpatient monitoring for successful and safe MEG data acquisition, (4) MEG mapping of eloquent cortex or interictal spike localization in the setting of tumor resection or other urgent neurosurgical intervention, and (5) international or long-distance patients, where outpatient MEG is not possible or practical. MEG contributed to surgical decision-making in the majority of our cases (32 of 34). Our clinical experience suggests that MEG should be considered on an inpatient basis in certain clinical circumstances, where MEG data can provide essential information regarding the localization of epileptogenic activity or eloquent cortex, and be used to develop a treatment plan for surgical management of children with complicated or intractable epilepsy.
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Affiliation(s)
- Michael W Watkins
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States
| | - Ekta G Shah
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States
| | - Michael E Funke
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States.,Department of Neurology, McGovern Medical School, Houston, TX, United States
| | - Stephanie Garcia-Tarodo
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States.,Pediatric Neurology Unit, Children's Hospital, Geneva University Hospitals, Geneva, Switzerland
| | - Manish N Shah
- Department of Neurosurgery, McGovern Medical School, Houston, TX, United States.,Division of Pediatric Neurosurgery, Department of Pediatric Surgery, McGovern Medical School, Houston, TX, United States
| | - Nitin Tandon
- Department of Neurosurgery, McGovern Medical School, Houston, TX, United States
| | - Fernando Maestu
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States.,Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politecnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Christopher Laohathai
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States
| | - David I Sandberg
- Department of Neurosurgery, McGovern Medical School, Houston, TX, United States.,Division of Pediatric Neurosurgery, Department of Pediatric Surgery, McGovern Medical School, Houston, TX, United States
| | - Jeremy Lankford
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States
| | - Stephen Thompson
- Department of Neurology, McGovern Medical School, Houston, TX, United States
| | - John Mosher
- Department of Neurology, McGovern Medical School, Houston, TX, United States
| | - Gretchen Von Allmen
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School, Houston, TX, United States.,Department of Neurology, McGovern Medical School, Houston, TX, United States
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17
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Otsubo H, Ogawa H, Pang E, Wong SM, Ibrahim GM, Widjaja E. A review of magnetoencephalography use in pediatric epilepsy: an update on best practice. Expert Rev Neurother 2021; 21:1225-1240. [PMID: 33780318 DOI: 10.1080/14737175.2021.1910024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: Magnetoencephalography (MEG) is a noninvasive technique that is used for presurgical evaluation of children with drug-resistant epilepsy (DRE).Areas covered: The contributions of MEG for localizing the epileptogenic zone are discussed, in particular in extra-temporal lobe epilepsy and focal cortical dysplasia, which are common in children, as well as in difficult to localize epilepsy such as operculo-insular epilepsy. Further, the authors review current evidence on MEG for mapping eloquent cortex, its performance, application in clinical practice, and potential challenges.Expert opinion: MEG could change the clinical management of children with DRE by directing placement of intracranial electrodes thereby enhancing their yield. With improved identification of a circumscribed epileptogenic zone, MEG could render more patients as suitable candidates for epilepsy surgery and increase utilization of surgery.
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Affiliation(s)
- Hiroshi Otsubo
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Hiroshi Ogawa
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Elizabeth Pang
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada.,Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada
| | - Simeon M Wong
- Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada
| | - George M Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Canada.,Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Elysa Widjaja
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada.,Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada.,Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
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18
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Bagić AI, Funke ME, Kirsch HE, Tenney JR, Zillgitt AJ, Burgess RC. The 10 Common Evidence-Supported Indications for MEG in Epilepsy Surgery: An Illustrated Compendium. J Clin Neurophysiol 2021; 37:483-497. [PMID: 33165222 DOI: 10.1097/wnp.0000000000000726] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Unfamiliarity with the indications for and benefits of magnetoencephalography (MEG) persists, even in the epilepsy community, and hinders its acceptance to clinical practice, despite the evidence. The wide treatment gap for patients with drug-resistant epilepsy and immense underutilization of epilepsy surgery had similar effects. Thus, educating referring physicians (epileptologists, neurologists, and neurosurgeons) both about the value of epilepsy surgery and about the potential benefits of MEG can achieve synergy and greatly improve the process of selecting surgical candidates. As a practical step toward a comprehensive educational process to benefit potential MEG users, current MEG referrers, and newcomers to MEG, the authors have elected to provide an illustrated guide to 10 everyday situations where MEG can help in the evaluation of people with drug-resistant epilepsy. They are as follows: (1) lacking or imprecise hypothesis regarding a seizure onset; (2) negative MRI with a mesial temporal onset suspected; (3) multiple lesions on MRI; (4) large lesion on MRI; (5) diagnostic or therapeutic reoperation; (6) ambiguous EEG findings suggestive of "bilateral" or "generalized" pattern; (7) intrasylvian onset suspected; (8) interhemispheric onset suspected; (9) insular onset suspected; and (10) negative (i.e., spikeless) EEG. Only their practical implementation and furtherance of personal and collective education will lead to the potentially impactful synergy of the two-MEG and epilepsy surgery. Thus, while fulfilling our mission as physicians, we must not forget that ignoring the wealth of evidence about the vast underutilization of epilepsy surgery - and about the usefulness and value of MEG in selecting surgical candidates - is far from benign neglect.
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Affiliation(s)
- Anto I Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania, U.S.A
| | - Michael E Funke
- MEG Center, McGovern Medical School, UT Houston, Houston, Texas, U.S.A
| | - Heidi E Kirsch
- UCSF Biomagnetic Imaging Laboratory, UCSF, San Francisco, California, U.S.A
| | - Jeffrey R Tenney
- MEG Center, Cincinnati Children's Hospital Medical Center , Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
| | - Andrew J Zillgitt
- Department of Neurology, Beaumont Health Adult Comprehensive Epilepsy Center, Neurosicence Center, Royal Oak, Michigan, U.S.A.; and
| | - Richard C Burgess
- Magnetoencephalography Laboratory, Cleveland Clinic Epilepsy Center, Cleveland, Ohio, U.S.A
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19
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20
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Abstract
Concise history of fascinating magnetoencephalography (MEG) technology and catalog of very selected milestone preclinical and clinical MEG studies are provided as the background. The focus is the societal context defining a journey of MEG to and through clinical practice and formation of the American Clinical MEG Society (ACMEGS). We aspired to provide an objective historic perspective and document contributions of many professionals while focusing on the role of ACMEGS in the growth and maturation of clinical MEG field. The ACMEGS was born (2006) out of inevitability to address two vital issues-fair reimbursement and proper clinical acceptance. A beacon of accountable MEG practice and utilization is now an expanding professional organization with the highest level of competence in practice of clinical MEG and clinical credibility. The ACMEGS facilitated a favorable disposition of insurances toward MEG in the United States by combining the national replication of the grassroots efforts and teaming up with the strategic partners-particularly the American Academy of Neurology (AAN), published two Position Statements (2009 and 2017), the world's only set of MEG Clinical Practice Guidelines (CPGs; 2011) and surveys of clinical MEG practice (2011 and 2020) and use (2020). In addition to the annual ACMEGS Course (2012), we directly engaged MEG practitioners through an Invitational Summit (2019). The Society remains focused on the improvements and expansion of clinical practice, education, clinical training, and constructive engagement of vendors in these issues and pivotal studies toward additional MEG indications. The ACMEGS not only had the critical role in the progress of Clinical MEG in the United States and beyond since 2006 but positioned itself as the field leader in the future.
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21
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Bagić AI, Rampp S. It is time to harmonize clinical MEG practice internationally. Clin Neurophysiol 2020; 131:1769-1771. [PMID: 32504938 DOI: 10.1016/j.clinph.2020.04.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 04/30/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Anto I Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, Suite 811, Kaufmann Medical Building, 3471 Fifth Ave, Pittsburgh, PA 15213, USA.
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital, Erlangen, Germany.
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22
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Clinical Magnetoencephalography Practice in the United States Ten Years Later: A Survey-Based Reappraisal. J Clin Neurophysiol 2020; 37:592-598. [DOI: 10.1097/wnp.0000000000000693] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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