Assessing REM Sleep in Mice Using Video Data
Assessing REM Sleep in Mice Using Video Data
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DOI：
10.5665/sleep.1712
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发表时间：
2012-03-01
期刊：
影响因子：
5.6
通讯作者：
Pack, Allan I.
中科院分区：
文献类型：
--
作者：
McShane, Blakeley B.;Galante, Raymond J.;Pack, Allan I.
研究目的：评估小鼠的睡眠及其亚阶段目前需要植入慢性电极以测量脑电图（EEG）和肌电图（EMG）。这对于高通量筛选并不理想。为了解决这个问题，我们提出了一种新的方法，基于数字视频分析。这种方法扩展了以前的方法，估计睡眠和觉醒没有EEG/EMG，以现在区分快速眼动（REM）从非REM（NREM）sleep.Design：研究中进行了8只雄性C57 BL/6 J小鼠。EEG/EMG记录24小时，并在10秒的时间段内手动评分。通过数字视频以10帧/秒连续记录小鼠行为。对于每个10秒时期从视频中提取六个变量（即，小鼠的速度、纵横比和面积的时期内平均值以及相同变量的时期内标准偏差），并用作我们模型的输入。测量和结果：我们专注于估计REM的特征（即，在REM中花费的时间、回合数和中值回合长度）以及在NREM和WAKE中花费的时间。我们还考虑了该模型相对于几种替代方法的逐时评分性能。我们的模型提供了一天中这些特征的良好估计，无论是在小鼠之间还是在单个小鼠中进行平均时，但时期-byepoch一致性并不那么好。当小鼠从NREM过渡到REM时，可能是由于REM的弛缓，因此允许我们的方法区分这两种状态。虽然快速眼动相对罕见，但我们的方法可以检测到它并评估快速眼动睡眠的量。
Study Objectives: Assessment of sleep and its substages in mice currently requires implantation of chronic electrodes for measurement of electroencephalogram (EEG) and electromyogram (EMG). This is not ideal for high-throughput screening. To address this deficiency, we present a novel method based on digital video analysis. This methodology extends previous approaches that estimate sleep and wakefulness without EEG/EMG in order to now discriminate rapid eye movement (REM) from non-REM (NREM) sleep.Design: Studies were conducted in 8 male C57BL/6J mice. EEG/EMG were recorded for 24 hours and manually scored in 10-second epochs. Mouse behavior was continuously recorded by digital video at 10 frames/second. Six variables were extracted from the video for each 10-second epoch (i.e., intraepoch mean of velocity, aspect ratio, and area of the mouse and intraepoch standard deviation of the same variables) and used as inputs for our model.Measurements and Results: We focus on estimating features of REM (i.e., time spent in REM, number of bouts, and median bout length) as well as time spent in NREM and WAKE. We also consider the model's epoch-by-epoch scoring performance relative to several alternative approaches. Our model provides good estimates of these features across the day both when averaged across mice and in individual mice, but the epoch-byepoch agreement is not as good.Conclusions: There are subtle changes in the area and shape (i.e., aspect ratio) of the mouse as it transitions from NREM to REM, likely due to the atonia of REM, thus allowing our methodology to discriminate these two states. Although REM is relatively rare, our methodology can detect it and assess the amount of REM sleep.