Kellogg School of Management, Northwestern University, Evanston, IL, USA.
Sleep. 2012 Mar 1;35(3):433-42. doi: 10.5665/sleep.1712.
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.
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.
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-by-epoch agreement is not as good.
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.
目前评估小鼠的睡眠及其亚期需要植入慢性电极来测量脑电图（EEG）和肌电图（EMG）。这对于高通量筛选来说并不理想。为了解决这个问题，我们提出了一种基于数字视频分析的新方法。这种方法扩展了以前的方法，以前的方法不需要 EEG/EMG 就可以估计睡眠和清醒状态，以便现在能够区分快速眼动（REM）和非快速眼动（NREM）睡眠。
本研究在 8 只雄性 C57BL/6J 小鼠中进行。记录 24 小时的 EEG/EMG，并以 10 秒为一个时间段进行手动评分。通过数字视频以每秒 10 帧的速度连续记录小鼠的行为。从视频中提取每个 10 秒时间段的 6 个变量（即，速度、纵横比和小鼠面积的时段内平均值以及相同变量的时段内标准差），并将其用作我们模型的输入。
我们专注于估计 REM（即 REM 时间、发作次数和中位发作长度）以及 NREM 和清醒时间的特征。我们还考虑了模型在相对于几种替代方法的逐段评分性能。我们的模型在一天中的表现都很好，无论是在小鼠平均水平还是在个体小鼠水平上，都能很好地估计这些特征，但逐段的一致性不是很好。
当小鼠从 NREM 过渡到 REM 时，其面积和形状（即纵横比）会发生细微变化，这可能是由于 REM 的弛缓所致，因此我们的方法能够区分这两种状态。尽管 REM 相对较少，但我们的方法可以检测到它并评估 REM 睡眠时间的长短。
