Abstract：
OBJECTIVE: Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally.
METHODS: We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types.
RESULTS: We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures.
CONCLUSIONS: This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.
METHODS: We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types.
RESULTS: We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures.
CONCLUSIONS: This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.
摘要：
目的：癫痫是一种常见的神经系统疾病，影响全球1%的人口。局灶性意识受损癫痫（FIAS）和局灶性至双侧强直阵挛性癫痫（FBTCS）的意识丧失可能是毁灭性的，但是机制还没有得到很好的理解。尽管已经注意到发作活动和发作间连通性的变化，局灶性癫痫发作(FAS)的网络状态，FIAS,FBTCS尚未通过网络措施进行全面评估。
方法：我们获得了74例立体脑电图（SEEG）患者的心电图数据。滑动窗带电源，功能连接，离析是在发作前计算的，ictal,和当前数据。五分钟的觉醒，快速眼动睡眠，深度睡眠也被提取出来。在一组同时患有SEEG和功能磁共振成像（fMRI）的患者中分析了皮质下唤醒结构的连通性。鉴于癫痫发作的自定义神经调节是基于癫痫发作类型的检测，使用卷积神经网络对癫痫发作类型进行分类.
结果：我们发现，在额叶皮质，与意识相关的区域，意识障碍癫痫发作(FIASs和FBTCSs)和深度睡眠的慢波δ(1-4Hz)频带功率均增加.然而,当采用网络措施时，我们发现，只有FIAS和深度睡眠表现出δ隔离的增加和γ隔离的减少。此外，我们发现,与对照组相比,只有FIASs患者在fMRI下的皮质下-新皮质功能连接降低.最后,我们的深度学习网络在曲线下有一个面积.75用于检测意识障碍癫痫发作.
结论：这项研究为FAS中的发病网络测量提供了新的见解，FIAS,和FBTCS。重要的是,尽管FIAS和FBTCS都会导致意识丧失，我们的结果表明,FIAS的发作网络变化与深度睡眠期间发生的变化非常相似.我们的结果可能为癫痫患者的意识保护提供新的神经调节策略。
方法：我们获得了74例立体脑电图（SEEG）患者的心电图数据。滑动窗带电源，功能连接，离析是在发作前计算的，ictal,和当前数据。五分钟的觉醒，快速眼动睡眠，深度睡眠也被提取出来。在一组同时患有SEEG和功能磁共振成像（fMRI）的患者中分析了皮质下唤醒结构的连通性。鉴于癫痫发作的自定义神经调节是基于癫痫发作类型的检测，使用卷积神经网络对癫痫发作类型进行分类.
结果：我们发现，在额叶皮质，与意识相关的区域，意识障碍癫痫发作(FIASs和FBTCSs)和深度睡眠的慢波δ(1-4Hz)频带功率均增加.然而,当采用网络措施时，我们发现，只有FIAS和深度睡眠表现出δ隔离的增加和γ隔离的减少。此外，我们发现,与对照组相比,只有FIASs患者在fMRI下的皮质下-新皮质功能连接降低.最后,我们的深度学习网络在曲线下有一个面积.75用于检测意识障碍癫痫发作.
结论：这项研究为FAS中的发病网络测量提供了新的见解，FIAS,和FBTCS。重要的是,尽管FIAS和FBTCS都会导致意识丧失，我们的结果表明,FIAS的发作网络变化与深度睡眠期间发生的变化非常相似.我们的结果可能为癫痫患者的意识保护提供新的神经调节策略。
