Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Peng, Syu-Jyun | en_US |
dc.contributor.author | Chou, Chien-Chen | en_US |
dc.contributor.author | Yu, Hsiang-Yu | en_US |
dc.contributor.author | Chen, Chien | en_US |
dc.contributor.author | Yen, Der-Jen | en_US |
dc.contributor.author | Kwan, Shang-Yeong | en_US |
dc.contributor.author | Hsu, Sanford P. C. | en_US |
dc.contributor.author | Lin, Chun-Fu | en_US |
dc.contributor.author | Chen, Hsin-Hung | en_US |
dc.contributor.author | Lee, Cheng-Chia | en_US |
dc.date.accessioned | 2019-12-13T01:09:54Z | - |
dc.date.available | 2019-12-13T01:09:54Z | - |
dc.date.issued | 2019-10-01 | en_US |
dc.identifier.issn | 0022-3085 | en_US |
dc.identifier.uri | http://dx.doi.org/10.3171/2018.6.JNS172844 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/153015 | - |
dc.description.abstract | OBJECTIVE In this study, the authors investigated high-frequency oscillation (HFO) networks during seizures in order to determine how HFOs spread from the focal cerebral cortex and become synchronized across various areas of the brain. METHODS All data were obtained from stereoelectroencephalography (SEEG) signals in patients with drug-resistant temporal lobe epilepsy (TLE). The authors calculated intercontact cross-coefficients between all pairs of contacts to construct HFO networks in 20 seizures that occurred in 5 patients. They then calculated HFO network topology metrics (i.e., network density and component size) after normalizing seizure duration data by dividing each seizure into 10 intervals of equal length (labeled I1-I10). RESULTS From the perspective of the dynamic topologies of cortical and subcortical HFO networks, the authors observed a significant increase in network density during intervals I5-I10. A significant increase was also observed in overall energy during intervals I3-I8. The results of subnetwork analysis revealed that the number of components continuously decreased following the onset of seizures, and those results were statistically significant during intervals I3-I10. Furthermore, the majority of nodes were connected to a single dominant component during the propagation of seizures, and the percentage of nodes within the largest component grew significantly until seizure termination. CONCLUSIONS The consistent topological changes that the authors observed suggest that TLE is affected by common epileptogenic patterns. Indeed, the findings help to elucidate the epileptogenic network that characterizes TLE, which may be of interest to researchers and physicians working to improve treatment modalities for epilepsy, including resection, cortical stimulation, and neuromodulation treatments that are responsive to network topologies. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | brain connectivity | en_US |
dc.subject | graph theory | en_US |
dc.subject | epileptogenic network | en_US |
dc.subject | topology | en_US |
dc.subject | epilepsy surgery | en_US |
dc.title | Ictal networks of temporal lobe epilepsy: views from high-frequency oscillations in stereoelectroencephalography | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3171/2018.6.JNS172844 | en_US |
dc.identifier.journal | JOURNAL OF NEUROSURGERY | en_US |
dc.citation.volume | 131 | en_US |
dc.citation.issue | 4 | en_US |
dc.citation.spage | 1086 | en_US |
dc.citation.epage | 1094 | en_US |
dc.contributor.department | 電子工程學系及電子研究所 | zh_TW |
dc.contributor.department | 生醫電子轉譯研究中心 | zh_TW |
dc.contributor.department | Department of Electronics Engineering and Institute of Electronics | en_US |
dc.contributor.department | Biomedical Electronics Translational Research Center | en_US |
dc.identifier.wosnumber | WOS:000490249600013 | en_US |
dc.citation.woscount | 0 | en_US |
Appears in Collections: | Articles |