Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone
Abstract Background While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of p...
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Nature Portfolio
2024-11-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00654-0 |
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| author | Behrang Fazli Besheli Zhiyi Sha Jay R. Gavvala Sacit Karamursel Michael Quach Chandra Prakash Swamy Amir Hossein Ayyoubi Alica M. Goldman Daniel J. Curry Sameer A. Sheth David Darrow Kai J. Miller David J. Francis Gregory A. Worrell Thomas R. Henry Nuri F. Ince |
| author_facet | Behrang Fazli Besheli Zhiyi Sha Jay R. Gavvala Sacit Karamursel Michael Quach Chandra Prakash Swamy Amir Hossein Ayyoubi Alica M. Goldman Daniel J. Curry Sameer A. Sheth David Darrow Kai J. Miller David J. Francis Gregory A. Worrell Thomas R. Henry Nuri F. Ince |
| author_sort | Behrang Fazli Besheli |
| collection | DOAJ |
| description | Abstract Background While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice. Methods In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data. This framework is utilized during intraoperative monitoring (IOM) while implanting electrodes and postoperatively in the epilepsy monitoring unit (EMU) before the respective surgery. Results Our framework demonstrates a remarkable ability to eliminate pseudo-HFOs in heavily corrupted neural data, achieving accuracy levels comparable to those obtained through expert visual inspection. It not only enhances SOZ localization accuracy of IOM to a level comparable to EMU but also successfully captures sHFO clusters within IOM recordings, exhibiting high specificity to the primary SOZ. Conclusions These findings suggest that intraoperative HFOs, when processed with computational intelligence, can be used as early feedback for SOZ tailoring surgery to guide electrode repositioning, enhancing the efficacy of the overall invasive therapy. |
| format | Article |
| id | doaj-art-16c3218c17fe4a649cb29fcf7612d65c |
| institution | DOAJ |
| issn | 2730-664X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-16c3218c17fe4a649cb29fcf7612d65c2025-08-20T02:49:09ZengNature PortfolioCommunications Medicine2730-664X2024-11-014111310.1038/s43856-024-00654-0Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zoneBehrang Fazli Besheli0Zhiyi Sha1Jay R. Gavvala2Sacit Karamursel3Michael Quach4Chandra Prakash Swamy5Amir Hossein Ayyoubi6Alica M. Goldman7Daniel J. Curry8Sameer A. Sheth9David Darrow10Kai J. Miller11David J. Francis12Gregory A. Worrell13Thomas R. Henry14Nuri F. Ince15Department of Neurologic Surgery, Mayo ClinicDepartment of Neurology, University of MinnesotaDepartment of Neurology, UT HealthDepartment of Physiology, School of Medicine, Koç ÜniversitesiDepartment of Neurology, Texas Children’s HospitalDepartment of Neurologic Surgery, Mayo ClinicDepartment of Neurologic Surgery, Mayo ClinicDepartment of Neurology-Neurophysiology, Baylor College of MedicineDepartment of Neurosurgery, Texas Children’s HospitalDepartment of Neurosurgery, Baylor College of MedicineDepartment of Neurosurgery, University of MinnesotaDepartment of Neurologic Surgery, Mayo ClinicDepartment of Psychology, University of HoustonDepartment of Neurology, Mayo ClinicDepartment of Neurology, University of MinnesotaDepartment of Neurologic Surgery, Mayo ClinicAbstract Background While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice. Methods In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data. This framework is utilized during intraoperative monitoring (IOM) while implanting electrodes and postoperatively in the epilepsy monitoring unit (EMU) before the respective surgery. Results Our framework demonstrates a remarkable ability to eliminate pseudo-HFOs in heavily corrupted neural data, achieving accuracy levels comparable to those obtained through expert visual inspection. It not only enhances SOZ localization accuracy of IOM to a level comparable to EMU but also successfully captures sHFO clusters within IOM recordings, exhibiting high specificity to the primary SOZ. Conclusions These findings suggest that intraoperative HFOs, when processed with computational intelligence, can be used as early feedback for SOZ tailoring surgery to guide electrode repositioning, enhancing the efficacy of the overall invasive therapy.https://doi.org/10.1038/s43856-024-00654-0 |
| spellingShingle | Behrang Fazli Besheli Zhiyi Sha Jay R. Gavvala Sacit Karamursel Michael Quach Chandra Prakash Swamy Amir Hossein Ayyoubi Alica M. Goldman Daniel J. Curry Sameer A. Sheth David Darrow Kai J. Miller David J. Francis Gregory A. Worrell Thomas R. Henry Nuri F. Ince Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone Communications Medicine |
| title | Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone |
| title_full | Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone |
| title_fullStr | Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone |
| title_full_unstemmed | Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone |
| title_short | Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone |
| title_sort | using high frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone |
| url | https://doi.org/10.1038/s43856-024-00654-0 |
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