Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation
Accurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the loca...
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| Format: | Article |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2021.677840/full |
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| author | Xiaonan Li Xiaonan Li Xiaonan Li Tao Yu Zhiwei Ren Xueyuan Wang Jiaqing Yan Xin Chen Xiaoming Yan Wei Wang Wei Wang Wei Wang Yue Xing Yue Xing Yue Xing Xianchang Zhang Herui Zhang Horace H. Loh Guojun Zhang Xiaofeng Yang Xiaofeng Yang Xiaofeng Yang |
| author_facet | Xiaonan Li Xiaonan Li Xiaonan Li Tao Yu Zhiwei Ren Xueyuan Wang Jiaqing Yan Xin Chen Xiaoming Yan Wei Wang Wei Wang Wei Wang Yue Xing Yue Xing Yue Xing Xianchang Zhang Herui Zhang Horace H. Loh Guojun Zhang Xiaofeng Yang Xiaofeng Yang Xiaofeng Yang |
| author_sort | Xiaonan Li |
| collection | DOAJ |
| description | Accurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the localization accuracy of the EZ. In this study, we aimed to explore the use of a multimodal method combining PET-MRI, fluid and white matter suppression (FLAWS)—a novel MRI sequence, and high-frequency oscillation (HFO) automated analysis to delineate EZ. We retrospectively collected 15 patients with refractory epilepsy who underwent surgery and used the above three methods to detect abnormal brain areas of all patients. We compared the PET-MRI, FLAWS, and HFO results with traditional methods to evaluate their diagnostic value. The sensitivities, specificities of locating the EZ, and marking extent removed versus not removed [RatioChann(ev)] of each method were compared with surgical outcome. We also tested the possibility of using different combinations to locate the EZ. The marked areas in every patient established using each method were also compared to determine the correlations among the three methods. The results showed that PET-MRI, FLAWS, and HFOs can provide more information about potential epileptic areas than traditional methods. When detecting the EZs, the sensitivities of PET-MRI, FLAWS, and HFOs were 68.75, 53.85, and 87.50%, and the specificities were 80.00, 33.33, and 100.00%. The RatioChann(ev) of HFO-marked contacts was significantly higher in patients with good outcome than those with poor outcome (p< 0.05). When intracranial electrodes covered all the abnormal areas indicated by neuroimaging with the overlapping EZs being completely removed referred to HFO analysis, patients could reach seizure-free (p < 0.01). The periphery of the lesion marked by neuroimaging may be epileptic, but not every lesion contributes to seizures. Therefore, approaches in multimodality can detect EZ more accurately, and HFO analysis may help in defining real epileptic areas that may be missed in the neuroimaging results. The implantation of intracranial electrodes guided by non-invasive PET-MRI and FLAWS findings as well as HFO analysis would be an optimized multimodal approach for locating EZ. |
| first_indexed | 2024-12-18T11:57:18Z |
| format | Article |
| id | doaj.art-2c07807258c94b7e9797e83ee723bac4 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-18T11:57:18Z |
| publishDate | 2021-06-01 |
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| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-2c07807258c94b7e9797e83ee723bac42022-12-21T21:09:01ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612021-06-011510.3389/fnhum.2021.677840677840Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency OscillationXiaonan Li0Xiaonan Li1Xiaonan Li2Tao Yu3Zhiwei Ren4Xueyuan Wang5Jiaqing Yan6Xin Chen7Xiaoming Yan8Wei Wang9Wei Wang10Wei Wang11Yue Xing12Yue Xing13Yue Xing14Xianchang Zhang15Herui Zhang16Horace H. Loh17Guojun Zhang18Xiaofeng Yang19Xiaofeng Yang20Xiaofeng Yang21Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaBioland Laboratory, Guangzhou, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaCollege of Electrical and Control Engineering, North China University of Technology, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaBioland Laboratory, Guangzhou, ChinaLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaBioland Laboratory, Guangzhou, ChinaMR Collaboration, Siemens Healthcare Ltd., Beijing, ChinaBioland Laboratory, Guangzhou, ChinaBioland Laboratory, Guangzhou, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, ChinaXuanwu Hospital, Capital Medical University, Beijing, ChinaBioland Laboratory, Guangzhou, ChinaAccurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the localization accuracy of the EZ. In this study, we aimed to explore the use of a multimodal method combining PET-MRI, fluid and white matter suppression (FLAWS)—a novel MRI sequence, and high-frequency oscillation (HFO) automated analysis to delineate EZ. We retrospectively collected 15 patients with refractory epilepsy who underwent surgery and used the above three methods to detect abnormal brain areas of all patients. We compared the PET-MRI, FLAWS, and HFO results with traditional methods to evaluate their diagnostic value. The sensitivities, specificities of locating the EZ, and marking extent removed versus not removed [RatioChann(ev)] of each method were compared with surgical outcome. We also tested the possibility of using different combinations to locate the EZ. The marked areas in every patient established using each method were also compared to determine the correlations among the three methods. The results showed that PET-MRI, FLAWS, and HFOs can provide more information about potential epileptic areas than traditional methods. When detecting the EZs, the sensitivities of PET-MRI, FLAWS, and HFOs were 68.75, 53.85, and 87.50%, and the specificities were 80.00, 33.33, and 100.00%. The RatioChann(ev) of HFO-marked contacts was significantly higher in patients with good outcome than those with poor outcome (p< 0.05). When intracranial electrodes covered all the abnormal areas indicated by neuroimaging with the overlapping EZs being completely removed referred to HFO analysis, patients could reach seizure-free (p < 0.01). The periphery of the lesion marked by neuroimaging may be epileptic, but not every lesion contributes to seizures. Therefore, approaches in multimodality can detect EZ more accurately, and HFO analysis may help in defining real epileptic areas that may be missed in the neuroimaging results. The implantation of intracranial electrodes guided by non-invasive PET-MRI and FLAWS findings as well as HFO analysis would be an optimized multimodal approach for locating EZ.https://www.frontiersin.org/articles/10.3389/fnhum.2021.677840/fullepileptogenic zoneneuroimaginghigh-frequency oscillationsPET-MRIFLAWSmultimodal method |
| spellingShingle | Xiaonan Li Xiaonan Li Xiaonan Li Tao Yu Zhiwei Ren Xueyuan Wang Jiaqing Yan Xin Chen Xiaoming Yan Wei Wang Wei Wang Wei Wang Yue Xing Yue Xing Yue Xing Xianchang Zhang Herui Zhang Horace H. Loh Guojun Zhang Xiaofeng Yang Xiaofeng Yang Xiaofeng Yang Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation Frontiers in Human Neuroscience epileptogenic zone neuroimaging high-frequency oscillations PET-MRI FLAWS multimodal method |
| title | Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation |
| title_full | Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation |
| title_fullStr | Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation |
| title_full_unstemmed | Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation |
| title_short | Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation |
| title_sort | localization of the epileptogenic zone by multimodal neuroimaging and high frequency oscillation |
| topic | epileptogenic zone neuroimaging high-frequency oscillations PET-MRI FLAWS multimodal method |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2021.677840/full |
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