Sophia R Zhai, Sridevi V Sarma, Kristin Gunnarsdottir, Nathan E Crone, Adam G Rouse, Jennifer J Cheng, Michael J Kinsman, Patrick Landazuri, Utku Uysal, Carol M Ulloa, Nathaniel Cameron, Sara Inati, Kareem A Zaghloul, Varina L Boerwinkle, Sarah Wyckoff, Niravkumar Barot, Jorge A González-Martínez, Joon Y Kang, Rachel June Smith
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It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific <i>in silico</i> dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. <b>Methods:</b> We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. <b>Results:</b> Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. <b>Discussion:</b> This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"4 ","pages":"1425625"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368849/pdf/","citationCount":"0","resultStr":"{\"title\":\"Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability.\",\"authors\":\"Sophia R Zhai, Sridevi V Sarma, Kristin Gunnarsdottir, Nathan E Crone, Adam G Rouse, Jennifer J Cheng, Michael J Kinsman, Patrick Landazuri, Utku Uysal, Carol M Ulloa, Nathaniel Cameron, Sara Inati, Kareem A Zaghloul, Varina L Boerwinkle, Sarah Wyckoff, Niravkumar Barot, Jorge A González-Martínez, Joon Y Kang, Rachel June Smith\",\"doi\":\"10.3389/fnetp.2024.1425625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Introduction:</b> For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. 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引用次数: 0
摘要
简介:对于耐药性癫痫患者来说,成功定位致痫区(EZ)并对其进行手术治疗可使癫痫发作痊愈。然而,由于目前还没有临床验证的 EZ 生物标志物,手术成功率差异很大。高致痫区通常会表现出皮质兴奋性增高,这可以通过单脉冲电刺激(SPES)来探测,即向脑组织输送短脉冲电流。研究表明,SPES 的高振幅反应可以定位 EZ 区域,表明兴奋性阈值降低。然而,在癫痫监测室(EMU)进行广泛的 SPES 需要耗费大量时间。因此,我们从发作间期颅内脑电图(iEEG)中建立了患者特异性的硅动态网络模型,以测试虚拟刺激是否能揭示潜在的网络信息,从而识别出与大脑物理刺激类似的高兴奋脑区。方法:我们对在五个中心接受评估的 69 名患者进行了虚拟刺激,并对术后一年的临床效果进行了评估。我们进一步研究了按手术结果分层的 14 例患者的 SPES iEEG 反应差异。结果临床标记的 EZ 皮层区域在虚拟刺激下的兴奋性高于非 EZ 区域,成功患者的差异最大,失败患者的差异很小。这些趋势在 EMU 进行的广泛 SPES 反应中也能观察到。最后,当兴奋性被用来预测通道是否在 EZ 中时,分类器的准确率达到了 91%。讨论本研究展示了通过虚拟刺激确定的兴奋性如何从发作间期颅内脑电图中捕捉到有关 EZ 的宝贵信息。
Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability.
Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.