Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis
The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dS...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10402121/ |
| _version_ | 1797238823961231360 |
|---|---|
| author | Jie Sun Yan Niu Yanqing Dong Xubin Wu Bin Wang Mengni Zhou Jie Xiang Jiuhong Ma |
| author_facet | Jie Sun Yan Niu Yanqing Dong Xubin Wu Bin Wang Mengni Zhou Jie Xiang Jiuhong Ma |
| author_sort | Jie Sun |
| collection | DOAJ |
| description | The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient’s seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance. |
| first_indexed | 2024-04-24T17:41:46Z |
| format | Article |
| id | doaj.art-714bf7f3da044500b8dc5b80f8c64fef |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-04-24T17:41:46Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-714bf7f3da044500b8dc5b80f8c64fef2024-03-27T23:00:05ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102024-01-01321324133210.1109/TNSRE.2024.335504510402121Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network AnalysisJie Sun0https://orcid.org/0000-0002-6470-9452Yan Niu1Yanqing Dong2Xubin Wu3Bin Wang4https://orcid.org/0000-0001-7771-5360Mengni Zhou5Jie Xiang6https://orcid.org/0000-0002-9758-6954Jiuhong Ma7College of Computer Science and Technology (College of Big Data), Taiyuan University of Technology, Taiyuan, ChinaCollege of Computer Science and Technology (College of Big Data), Taiyuan University of Technology, Taiyuan, ChinaCollege of Computer Science and Technology (College of Big Data), Taiyuan University of Technology, Taiyuan, ChinaCollege of Computer Science and Technology (College of Big Data), Taiyuan University of Technology, Taiyuan, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaCollege of Computer Science and Technology (College of Big Data), Taiyuan University of Technology, Taiyuan, ChinaNeurosurgery Department, Shanxi Provincial People's Hospital, Taiyuan, ChinaThe variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient’s seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance.https://ieeexplore.ieee.org/document/10402121/Epilepsypropagation networkdynamic step effective networkdissimilarity |
| spellingShingle | Jie Sun Yan Niu Yanqing Dong Xubin Wu Bin Wang Mengni Zhou Jie Xiang Jiuhong Ma Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis IEEE Transactions on Neural Systems and Rehabilitation Engineering Epilepsy propagation network dynamic step effective network dissimilarity |
| title | Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis |
| title_full | Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis |
| title_fullStr | Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis |
| title_full_unstemmed | Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis |
| title_short | Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis |
| title_sort | seizure pathways changes at the subject specific level via dynamic step effective network analysis |
| topic | Epilepsy propagation network dynamic step effective network dissimilarity |
| url | https://ieeexplore.ieee.org/document/10402121/ |
| work_keys_str_mv | AT jiesun seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis AT yanniu seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis AT yanqingdong seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis AT xubinwu seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis AT binwang seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis AT mengnizhou seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis AT jiexiang seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis AT jiuhongma seizurepathwayschangesatthesubjectspecificlevelviadynamicstepeffectivenetworkanalysis |