Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 2 of 16

Sleep is observed in most animals, which suggests it subserves a
fundamental process associated with adaptive biological functions.
However, the evidence to directly associate sleep with a specific function
is lacking, in part because sleep is not a single process in many animals.
In humans and other mammals, different sleep stages have traditionally
been identified using electroencephalograms (EEGs), but such an approach
is not feasible in different animals such as insects. Here, we perform
long-term multichannel local field potential (LFP) recordings in the
brains of behaving flies undergoing spontaneous sleep bouts. We developed
protocols to allow for consistent spatial recordings of LFPs across
multiple flies, allowing us to compare the LFP activity across awake and
sleep periods and further compare the same to induced sleep. Using machine
learning, we uncover the existence of distinct temporal stages of sleep
and explore the associated spatial and spectral features across the fly
brain. Further, we analyze the electrophysiological correlates of
micro-behaviours associated with certain sleep stages. We confirm the
existence of a distinct sleep stage associated with rhythmic proboscis
extensions and show that spectral features of this sleep-related behavior
differ significantly from those associated with the same behavior during
wakefulness, indicating a dissociation between behavior and the brain
states wherein these behaviors reside.

睡眠现象存在于大多数动物中，这表明它服务于与适应性生物学功能相关的基础过程。然而，直接将睡眠与特定功能关联的证据仍显不足，部分原因在于睡眠在许多动物中并非单一过程。在人类及其他哺乳动物中，传统上通过脑电图（EEG）识别不同睡眠阶段，但这种方法在昆虫等其他动物中并不适用。在此，我们对处于自发睡眠周期的行为学状态果蝇的大脑进行了长期多通道局部场电位（LFP）记录。我们开发了实验方案，以实现跨多只果蝇的LFP一致空间记录，从而能够比较清醒期与睡眠期的LFP活动，并进一步与诱导睡眠状态下的活动进行对比。通过机器学习，我们揭示了睡眠存在不同时间阶段，并探索了果蝇大脑中与这些阶段相关的空间及频谱特征。此外，我们分析了与特定睡眠阶段相关的微行为的电生理关联。我们证实存在一种与节律性口器伸展相关的独特睡眠阶段，且该睡眠相关行为的频谱特征与清醒状态下相同行为的特征存在显著差异，这表明行为与其所处的脑状态之间存在分离。