Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht Utrecht, Netherlands.
Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht Utrecht, Netherlands ; Biomedical MR Imaging and Spectroscopy Group, Image Sciences Institute, University Medical Center Utrecht Utrecht, Netherlands.
Front Syst Neurosci. 2014 Apr 29;8:67. doi: 10.3389/fnsys.2014.00067. eCollection 2014.
Electroencephalography (EEG) recordings after sleep deprivation increase the diagnostic yield in patients suspected of epilepsy if the routine EEG remains inconclusive. Sleep deprivation is associated with increased interictal EEG abnormalities in patients with epilepsy, but the exact mechanism is unknown. In this feasibility study, we used a network analytical approach to provide novel insights into this clinical observation. The aim was to characterize the effect of sleep deprivation on the interictal functional network organization using a unique dataset of paired routine and sleep deprivation recordings in patients and controls. We included 21 children referred to the first seizure clinic of our center with suspected new onset focal epilepsy in whom a routine interictal and a sleep deprivation EEG (SD-EEG) were performed. Seventeen children, in whom the diagnosis of epilepsy was excluded, served as controls. For both time points weighted functional networks were constructed based on interictal artifact free time-series. Routine and sleep deprivation networks were characterized at different frequency bands using minimum spanning tree (MST) measures (leaf number and diameter) and classical measures of integration (path length) and segregation (clustering coefficient). A significant interaction was found for leaf number and diameter between patients and controls after sleep deprivation: patients showed a shift toward a more path-like MST network whereas controls showed a shift toward a more star-like MST network. This shift in network organization after sleep deprivation in patients is in accordance with previous studies showing a more regular network organization in the ictal state and might relate to the increased epileptiform abnormalities found in patients after sleep deprivation. Larger studies are needed to verify these results. Finally, MST measures were more sensitive in detecting network changes as compared to the classical measures of integration and segregation.
睡眠剥夺后的脑电图(EEG)记录,如果常规 EEG 结果仍不确定,则可以提高疑似癫痫患者的诊断效果。睡眠剥夺与癫痫患者的发作间期 EEG 异常增加有关,但确切的机制尚不清楚。在这项可行性研究中,我们使用网络分析方法为这一临床观察提供了新的见解。目的是使用患者和对照组的常规和睡眠剥夺记录的独特数据集,通过特征描述睡眠剥夺对发作间期功能网络组织的影响。我们纳入了 21 名因疑似新发局灶性癫痫而被转至我们中心首次癫痫诊所的儿童,他们进行了常规发作间期和睡眠剥夺 EEG(SD-EEG)。17 名癫痫诊断被排除的儿童作为对照组。对于这两个时间点,基于发作间期无伪迹时间序列构建了加权功能网络。使用最小生成树(MST)测度(叶数和直径)和经典的整合(路径长度)和分离(聚类系数)测度来描述常规和睡眠剥夺网络。我们发现睡眠剥夺后患者和对照组之间的叶数和直径存在显著的交互作用:患者的 MST 网络向更类似于路径的网络转变,而对照组的 MST 网络向更类似于星状的网络转变。患者在睡眠剥夺后网络组织的这种转变与之前的研究一致,这些研究表明在发作状态下网络组织更加规则,并且可能与睡眠剥夺后患者中发现的更多癫痫样异常有关。需要更大的研究来验证这些结果。最后,与整合和分离的经典测度相比,MST 测度在检测网络变化方面更敏感。