Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses
Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain structures with fine temporal resolutions to better investigate the origin of epileptic brain activities. Although oscillatory patterns from different frequency bands and functional connectivity computed from the SEEG...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923005906 |
| _version_ | 1827596469556740096 |
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| author | Yonglin Dou Jing Xia Mengmeng Fu Yunpeng Cai Xianghong Meng Yang Zhan |
| author_facet | Yonglin Dou Jing Xia Mengmeng Fu Yunpeng Cai Xianghong Meng Yang Zhan |
| author_sort | Yonglin Dou |
| collection | DOAJ |
| description | Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain structures with fine temporal resolutions to better investigate the origin of epileptic brain activities. Although oscillatory patterns from different frequency bands and functional connectivity computed from the SEEG datasets are employed to study the epileptic zones, direct electrical stimulation-evoked electrophysiological recordings of synaptic responses, namely cortical-cortical evoked potentials (CCEPs), from the same SEEG electrodes are not explored for the localization of epileptic zones. Here we proposed a two-stream model with unsupervised learning and graph convolutional network tailored to the SEEG and CCEP datasets in individual patients to perform localization of epileptic zones. We compared our localization results with the clinically marked electrode sites determined for surgical resections. Our model had good classification capability when compared to other state-of-the-art methods. Furthermore, based on our prediction results we performed group-level brain-area mapping analysis for temporal, frontal and parietal epilepsy patients and found that epileptic and non-epileptic brain networks were distinct in patients with different types of focal epilepsy. Our unsupervised data-driven model provides personalized localization analysis for the epileptic zones. The epileptic and non-epileptic brain areas disclosed by the prediction model provide novel insights into the network-level pathological characteristics of epilepsy. |
| first_indexed | 2024-03-09T03:10:59Z |
| format | Article |
| id | doaj.art-74eff7408ac3464a8b1d801ad9959b04 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-09T03:10:59Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-74eff7408ac3464a8b1d801ad9959b042023-12-04T05:21:24ZengElsevierNeuroImage1095-95722023-12-01284120439Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responsesYonglin Dou0Jing Xia1Mengmeng Fu2Yunpeng Cai3Xianghong Meng4Yang Zhan5The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaThe Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, ChinaInstitute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China; Corresponding authors.The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China; Corresponding authors.Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain structures with fine temporal resolutions to better investigate the origin of epileptic brain activities. Although oscillatory patterns from different frequency bands and functional connectivity computed from the SEEG datasets are employed to study the epileptic zones, direct electrical stimulation-evoked electrophysiological recordings of synaptic responses, namely cortical-cortical evoked potentials (CCEPs), from the same SEEG electrodes are not explored for the localization of epileptic zones. Here we proposed a two-stream model with unsupervised learning and graph convolutional network tailored to the SEEG and CCEP datasets in individual patients to perform localization of epileptic zones. We compared our localization results with the clinically marked electrode sites determined for surgical resections. Our model had good classification capability when compared to other state-of-the-art methods. Furthermore, based on our prediction results we performed group-level brain-area mapping analysis for temporal, frontal and parietal epilepsy patients and found that epileptic and non-epileptic brain networks were distinct in patients with different types of focal epilepsy. Our unsupervised data-driven model provides personalized localization analysis for the epileptic zones. The epileptic and non-epileptic brain areas disclosed by the prediction model provide novel insights into the network-level pathological characteristics of epilepsy.http://www.sciencedirect.com/science/article/pii/S1053811923005906Deep networkUnsupervised learningSeizure onset zonesAdaptive graph convolutionSeizure network |
| spellingShingle | Yonglin Dou Jing Xia Mengmeng Fu Yunpeng Cai Xianghong Meng Yang Zhan Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses NeuroImage Deep network Unsupervised learning Seizure onset zones Adaptive graph convolution Seizure network |
| title | Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses |
| title_full | Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses |
| title_fullStr | Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses |
| title_full_unstemmed | Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses |
| title_short | Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses |
| title_sort | identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses |
| topic | Deep network Unsupervised learning Seizure onset zones Adaptive graph convolution Seizure network |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923005906 |
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