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dc.contributor.authorRen, Ye
dc.contributor.authorCong, Fengyu
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorWang, Yuping
dc.contributor.authorLi, Xiaoli
dc.contributor.authorZhang, Ruihua
dc.date.accessioned2019-05-28T07:01:23Z
dc.date.available2020-01-26T22:35:34Z
dc.date.issued2019
dc.identifier.citationRen, Y., Cong, F., Ristaniemi, T., Wang, Y., Li, X., & Zhang, R. (2019). Transient seizure onset network for localization of epileptogenic zone : effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy. <i>Journal of Neurology</i>, <i>266</i>(4), 844-859. <a href="https://doi.org/10.1007/s00415-019-09204-4" target="_blank">https://doi.org/10.1007/s00415-019-09204-4</a>
dc.identifier.otherCONVID_28888513
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64244
dc.description.abstractObjective: Abnormal and dynamic epileptogenic networks cause difficulties for clinical epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ. Methods: We used electrocorticogram recordings in the temporal lobe and hippocampus from seven patients with temporal lobe epilepsy to characterize the effective connectivity dynamics at a high temporal resolution using the full-frequency adaptive directed transfer function (ffADTF) measure and five graph metrics, i.e., the out-degree (OD), closeness centrality (CC), betweenness centrality (BC), clustering coefficient (C), and local efficiency (LE). The ffADTF effective connectivity network was calculated and described in five frequency bands (δ, θ, α, β, and γ) and five seizure periods (pre-seizure, early seizure, mid-seizure, late seizure, and post-seizure). The cortical areas with high values of graph metrics in the transient seizure onset network were compared with the SOZ and EZ identified by clinical epileptologists and the results of epilepsy resection surgeries. Results: Origination and propagation of epileptic activity were observed in the high time resolution ffADTF effective connectivity network throughout the entire seizure period. The seizure-specific transient seizure onset ffADTF network that emerged at seizure onset time remained for approximately 20–50 ms with strong connections generated from both SOZ and EZ. The values of graph metrics in the SOZ and EZ were significantly larger than that in the other cortical areas. More cortical areas with the highest mean of graph metrics were the same as the clinically determined SOZ in the low-frequency δ and θ bands and in Engel Class I patients than in higher frequency α, β, and γ bands and in Engel Class II and III patients. The OD and C were more likely to localize the SOZ and EZ than CC, BC, and LE in the transient seizure onset network. Conclusion: The high temporal resolution ffADTF effective connectivity analysis combined with the graph theoretical analysis helps us to understand how epileptic activity is generated and propagated during the seizure period. The newly discovered seizure-specific transient seizure onset network could be an important biomarker and a promising tool for more precise localization of the SOZ and EZ in preoperative evaluations.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Berlin Heidelberg
dc.relation.ispartofseriesJournal of Neurology
dc.rightsIn Copyright
dc.subject.otheradaptive directed transfer function
dc.subject.othergraph metric
dc.subject.otherbrain connectivity
dc.subject.otherseizure onset zone
dc.subject.otherepileptogenic zone
dc.titleTransient seizure onset network for localization of epileptogenic zone : effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy
dc.typeresearch article
dc.identifier.urnURN:NBN:fi:jyu-201905242779
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMonitieteinen aivotutkimuskeskusfi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineCentre for Interdisciplinary Brain Researchen
dc.contributor.oppiaineSchool of Wellbeingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-05-24T06:15:25Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange844-859
dc.relation.issn0340-5354
dc.relation.numberinseries4
dc.relation.volume266
dc.type.versionacceptedVersion
dc.rights.copyright© Springer-Verlag GmbH Germany, part of Springer Nature 2019
dc.rights.accesslevelopenAccessfi
dc.type.publicationarticle
dc.subject.ysoaivotutkimus
dc.subject.ysoverkkoteoria
dc.subject.ysoepilepsia
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p2543
jyx.subject.urihttp://www.yso.fi/onto/yso/p9413
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/s00415-019-09204-4
dc.type.okmA1


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