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Mendoza T, Trevino CL, Shrey DW, Lin JJ, Sen-Gupta I, Lopour BA. Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization. Clin Neurophysiol 2024; 164:30-39. [PMID: 38843758 DOI: 10.1016/j.clinph.2024.05.010] [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: 01/11/2024] [Revised: 02/28/2024] [Accepted: 05/20/2024] [Indexed: 07/15/2024]
Abstract
OBJECTIVE High frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. METHODS We detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. RESULTS SOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. CONCLUSIONS SOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SIGNIFICANCE Visual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.
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Affiliation(s)
- Trisha Mendoza
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Casey L Trevino
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Daniel W Shrey
- Division of Neurology, Children's Hospital of Orange County, Orange, CA, USA; Department of Pediatrics, University of California, Irvine, Orange, CA, USA
| | - Jack J Lin
- UC Davis Comprehensive Epilepsy Program, Department of Neurology, Davis, CA, USA; UC Davis Center for Mind and Brain, Davis, CA, USA
| | - Indranil Sen-Gupta
- Comprehensive Epilepsy Program, Department of Neurology, University of California, Irvine, Irvine, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
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Frauscher B, Rossetti AO, Beniczky S. Recent advances in clinical electroencephalography. Curr Opin Neurol 2024; 37:134-140. [PMID: 38230652 DOI: 10.1097/wco.0000000000001246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
PURPOSE OF REVIEW Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients. RECENT FINDINGS Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field. SUMMARY This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.
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Affiliation(s)
- Birgit Frauscher
- Department of Neurology, Duke University Medical Center & Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
| | - Andrea O Rossetti
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund
- Aarhus University Hospital, Aarhus, Denmark
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Nunez MD, Fernandez K, Srinivasan R, Vandekerckhove J. A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Behav Res Methods 2024:10.3758/s13428-023-02331-x. [PMID: 38409458 DOI: 10.3758/s13428-023-02331-x] [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] [Accepted: 12/21/2023] [Indexed: 02/28/2024]
Abstract
We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.
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Affiliation(s)
- Michael D Nunez
- Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.
| | - Kianté Fernandez
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Joachim Vandekerckhove
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
- Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
- Department of Statistics, University of California, Irvine, CA, USA
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Owen TW, Janiukstyte V, Hall GR, Chowdhury FA, Diehl B, McEvoy A, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Wang Y, Taylor PN. Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome. Brain Commun 2023; 5:fcad292. [PMID: 37953844 PMCID: PMC10636564 DOI: 10.1093/braincomms/fcad292] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/24/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
Intracranial EEG is the gold standard technique for epileptogenic zone localization but requires a preconceived hypothesis of the location of the epileptogenic tissue. This placement is guided by qualitative interpretations of seizure semiology, MRI, EEG and other imaging modalities, such as magnetoencephalography. Quantitative abnormality mapping using magnetoencephalography has recently been shown to have potential clinical value. We hypothesized that if quantifiable magnetoencephalography abnormalities were sampled by intracranial EEG, then patients' post-resection seizure outcome may be better. Thirty-two individuals with refractory neocortical epilepsy underwent magnetoencephalography and subsequent intracranial EEG recordings as part of presurgical evaluation. Eyes-closed resting-state interictal magnetoencephalography band power abnormality maps were derived from 70 healthy controls as a normative baseline. Magnetoencephalography abnormality maps were compared to intracranial EEG electrode implantation, with the spatial overlap of intracranial EEG electrode placement and cerebral magnetoencephalography abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue and subsequent resection of the strongest abnormalities determined by magnetoencephalography and intracranial EEG corresponded to surgical success. We used the area under the receiver operating characteristic curve as a measure of effect size. Intracranial electrodes were implanted in brain tissue with the most abnormal magnetoencephalography findings-in individuals that were seizure-free postoperatively (T = 3.9, P = 0.001) but not in those who did not become seizure-free. The overlap between magnetoencephalography abnormalities and electrode placement distinguished surgical outcome groups moderately well (area under the receiver operating characteristic curve = 0.68). In isolation, the resection of the strongest abnormalities as defined by magnetoencephalography and intracranial EEG separated surgical outcome groups well, area under the receiver operating characteristic curve = 0.71 and area under the receiver operating characteristic curve = 0.74, respectively. A model incorporating all three features separated surgical outcome groups best (area under the receiver operating characteristic curve = 0.80). Intracranial EEG is a key tool to delineate the epileptogenic zone and help render individuals seizure-free postoperatively. We showed that data-driven abnormality maps derived from resting-state magnetoencephalography recordings demonstrate clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Additionally, our predictive model of postoperative seizure freedom, which leverages both magnetoencephalography and intracranial EEG recordings, could aid patient counselling of expected outcome.
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Affiliation(s)
- Thomas W Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Vytene Janiukstyte
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Gerard R Hall
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Andrew McEvoy
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Fergus Rugg-Gunn
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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Al-Bakri AF, Martinek R, Pelc M, Zygarlicki J, Kawala-Sterniuk A. Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples. SENSORS (BASEL, SWITZERLAND) 2022; 22:7522. [PMID: 36236621 PMCID: PMC9571066 DOI: 10.3390/s22197522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/20/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.
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Affiliation(s)
- Amir F. Al-Bakri
- Department of Biomedical Engineering, College of Engineering, University of Babylon, Hillah 51001, Iraq
| | - Radek Martinek
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava–Poruba, Czech Republic
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- School of Computing and Mathematical Sciences, University of Greenwich, Park Row, London SE10 9LS, UK
| | - Jarosław Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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