Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA.
Sleep. 2019 Feb 1;42(2). doi: 10.1093/sleep/zsy218.
Sleep spindles are abnormal in several neuropsychiatric conditions and have been implicated in associated cognitive symptoms. Accordingly, there is growing interest in elucidating the pathophysiology behind spindle abnormalities using rodent models of such disorders. However, whether sleep spindles can reliably be detected in mouse electroencephalography (EEG) is controversial necessitating careful validation of spindle detection and analysis techniques.
Manual spindle detection procedures were developed and optimized to generate an algorithm for automated detection of events from mouse cortical EEG. Accuracy and external validity of this algorithm were then assayed via comparison to sigma band (10-15 Hz) power analysis, a proxy for sleep spindles, and pharmacological manipulations.
We found manual spindle identification in raw mouse EEG unreliable, leading to low agreement between human scorers as determined by F1-score (0.26 ± 0.07). Thus, we concluded it is not possible to reliably score mouse spindles manually using unprocessed EEG data. Manual scoring from processed EEG data (filtered, cubed root-mean-squared), enabled reliable detection between human scorers, and between human scorers and algorithm (F1-score > 0.95). Algorithmically detected spindles correlated with changes in sigma-power and were altered by the following conditions: sleep-wake state changes, transitions between NREM and REM sleep, and application of the hypnotic drug zolpidem (10 mg/kg, intraperitoneal).
Here we describe and validate an automated paradigm for rapid and reliable detection of spindles from mouse EEG recordings. This technique provides a powerful tool to facilitate investigations of the mechanisms of spindle generation, as well as spindle alterations evident in mouse models of neuropsychiatric disorders.
睡眠纺锤波在几种神经精神疾病中异常，并与相关认知症状有关。因此，人们越来越感兴趣的是使用这些疾病的啮齿动物模型来阐明纺锤波异常背后的病理生理学。然而，睡眠纺锤波是否能在小鼠脑电图（EEG）中可靠地检测到，这是有争议的，因此需要仔细验证纺锤波检测和分析技术。
开发并优化了手动纺锤波检测程序，以生成一种从小鼠皮质 EEG 中自动检测事件的算法。然后通过与 sigma 频带（10-15 Hz）功率分析（睡眠纺锤波的替代物）和药理学处理的比较，来评估该算法的准确性和外部有效性。
我们发现原始小鼠 EEG 中的手动纺锤波识别不可靠，导致人类评分者之间的一致性低，F1 评分（0.26 ± 0.07）。因此，我们得出结论，不可能使用未经处理的 EEG 数据可靠地手动评分小鼠纺锤波。从经过处理的 EEG 数据（滤波、立方根均方根）手动评分，使人类评分者之间以及人类评分者与算法之间能够可靠地检测到，F1 评分>0.95。算法检测到的纺锤波与 sigma 功率的变化相关，并且受以下条件的影响：睡眠-觉醒状态的变化、非快速眼动（NREM）和快速眼动（REM）睡眠之间的转换，以及催眠药物唑吡坦（10mg/kg，腹腔内注射）的应用。
在这里，我们描述并验证了一种从小鼠 EEG 记录中快速可靠地检测纺锤波的自动化范式。这项技术为研究纺锤波产生机制以及在神经精神疾病小鼠模型中明显的纺锤波改变提供了有力的工具。
