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dc.contributor.authorPeng, Syu-Jyunen_US
dc.contributor.authorChou, Chien-Chenen_US
dc.contributor.authorYu, Hsiang-Yuen_US
dc.contributor.authorChen, Chienen_US
dc.contributor.authorYen, Der-Jenen_US
dc.contributor.authorKwan, Shang-Yeongen_US
dc.contributor.authorHsu, Sanford P. C.en_US
dc.contributor.authorLin, Chun-Fuen_US
dc.contributor.authorChen, Hsin-Hungen_US
dc.contributor.authorLee, Cheng-Chiaen_US
dc.date.accessioned2019-12-13T01:09:54Z-
dc.date.available2019-12-13T01:09:54Z-
dc.date.issued2019-10-01en_US
dc.identifier.issn0022-3085en_US
dc.identifier.urihttp://dx.doi.org/10.3171/2018.6.JNS172844en_US
dc.identifier.urihttp://hdl.handle.net/11536/153015-
dc.description.abstractOBJECTIVE 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.isoen_USen_US
dc.subjectbrain connectivityen_US
dc.subjectgraph theoryen_US
dc.subjectepileptogenic networken_US
dc.subjecttopologyen_US
dc.subjectepilepsy surgeryen_US
dc.titleIctal networks of temporal lobe epilepsy: views from high-frequency oscillations in stereoelectroencephalographyen_US
dc.typeArticleen_US
dc.identifier.doi10.3171/2018.6.JNS172844en_US
dc.identifier.journalJOURNAL OF NEUROSURGERYen_US
dc.citation.volume131en_US
dc.citation.issue4en_US
dc.citation.spage1086en_US
dc.citation.epage1094en_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.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.contributor.departmentBiomedical Electronics Translational Research Centeren_US
dc.identifier.wosnumberWOS:000490249600013en_US
dc.citation.woscount0en_US
Appears in Collections:Articles