Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 6 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.

绝大多数动物均存在睡眠现象，这表明其维系着与适应性生物学功能相关的基础性生理过程。然而，目前仍缺乏将睡眠与特定功能直接关联的证据，部分原因在于许多动物的睡眠并非单一的生理过程。在人类及其他哺乳动物中，传统上通过脑电图（electroencephalograms, EEGs）来区分不同的睡眠阶段，但该方法在昆虫等其他动物中并不可行。本研究中，我们对处于自发睡眠时段的活动果蝇的脑部开展了长期多通道局部场电位（local field potential, LFP）记录。我们开发了可在多只果蝇间实现稳定空间LFP记录的实验方案，借此能够比较清醒与睡眠状态下的LFP活动，并进一步将其与诱导睡眠的LFP活动进行对比。借助机器学习方法，我们发现果蝇存在不同的睡眠时域阶段，并探究了全脑范围内与之相关的空间与频谱特征。此外，我们还分析了与特定睡眠阶段相关的微行为的电生理关联特征。我们证实了一类与节律性喙部伸缩相关的独特睡眠阶段的存在，并发现该睡眠相关行为的频谱特征与清醒状态下同一种行为的频谱特征存在显著差异，这表明行为与其所处脑状态之间存在分离现象。