Wright Samantha, Wallace Eli, Hwang Youngdeok, Maganti Rama
Department of Neurology, University of Wisconsin School of Medicine and Public Health, 1685 Highland Avenue, WI 53705-2281, USA.
IBM Thomas J. Watson Research Center, 1101 Route 134 Kitchawan Rd, Yorktown Heights, NY 10598, USA.
Epilepsy Behav. 2016 Feb;55:24-9. doi: 10.1016/j.yebeh.2015.11.028. Epub 2015 Dec 24.
This study was undertaken to describe seizure phenotypes, natural progression, sleep-wake patterns, as well as periodicity of seizures in Kcna-1 null mutant mice. These mice were implanted with epidural electroencephalography (EEG) and electromyography (EMG) electrodes, and simultaneous video-EEG recordings were obtained while animals were individually housed under either diurnal (LD) condition or constant darkness (DD) over ten days of recording. The video-EEG data were analyzed to identify electrographic and behavioral phenotypes and natural progression and to examine the periodicity of seizures. Sleep-wake patterns were analyzed to understand the distribution and onset of seizures across the sleep-wake cycle. Four electrographically and behaviorally distinct seizure types were observed. Regardless of lighting condition that animals were housed in, Kcna-1 null mice initially expressed only a few of the most severe seizure types that progressively increased in frequency and decreased in seizure severity. In addition, a circadian periodicity was noted, with seizures peaking in the first 12h of the Zeitgeber time (ZT) cycle, regardless of lighting conditions. Interestingly, seizure onset differed between lighting conditions where more seizures arose out of sleep in LD conditions, whereas under DD conditions, the majority occurred out of the wakeful state. We suggest that this model be used to understand the circadian pattern of seizures as well as the pathophysiological implications of sleep and circadian disturbances in limbic epilepsies.
本研究旨在描述Kcna-1基因敲除突变小鼠的癫痫发作表型、自然病程、睡眠-觉醒模式以及癫痫发作的周期性。给这些小鼠植入硬膜外脑电图（EEG）和肌电图（EMG）电极，并在动物分别饲养于昼夜交替（LD）条件或持续黑暗（DD）条件下进行为期十天的记录时，同时获取视频脑电图记录。对视频脑电图数据进行分析，以识别脑电图和行为表型、自然病程，并检查癫痫发作的周期性。分析睡眠-觉醒模式，以了解癫痫发作在睡眠-觉醒周期中的分布和发作情况。观察到四种在脑电图和行为上不同的癫痫发作类型。无论动物饲养的光照条件如何，Kcna-1基因敲除小鼠最初仅表现出少数几种最严重的癫痫发作类型，这些类型的发作频率逐渐增加，发作严重程度逐渐降低。此外，还注意到昼夜节律性，无论光照条件如何，癫痫发作在昼夜时间（ZT）周期的前12小时达到峰值。有趣的是，光照条件下癫痫发作的起始有所不同，在LD条件下更多癫痫发作源于睡眠，而在DD条件下，大多数发作源于清醒状态。我们建议使用该模型来理解癫痫发作的昼夜节律模式以及边缘性癫痫中睡眠和昼夜节律紊乱的病理生理学意义。
