Functional characterization of wake/sleep cycles

This report presents a novel methodology for the functional characterization ofwakefulness and sleep cycles based on large-field-of-view two-photon calciumimaging. The approach aims to investigate spatiotemporal patterns of neuronalactivity without relying on traditional electrophysiological techniques such as EEGor LFP. Using head-fixed mice expressing the calcium indicator GCaMP6f in corticalLayer 2/3 neurons, brain states were inferred through pupil size dynamics, whichprovided a reliable marker to differentiate between wakefulness and NREM sleep.Machine-learning-based analytical methods, including Hidden Markov Models(HMM) for identifying latent brain states, were developed to extract distinctpatterns of neuronal activity. The results revealed that neuronal activity alternatesbetween different modes both during wakefulness and NREM sleep, suggestingthat cortical activity across the sleep cycle is more heterogeneous than previouslythought. While neuropil-based analysis did not reliably distinguish wakefulnessfrom sleep, the combination of pupil-based classification and HMM successfullycaptured the fragmented nature of rodent sleep. The methodology represents asignificant advancement in the study of sleep-related neuronal dynamics, offeringa scalable framework for investigating brain states in both animal models andcerebral organoids. Future applications will further explore the spatial organizationof activity modes and their relationship across wake and sleep, with potentialimplications for understanding sleep disruptions in neurodegenerative conditionssuch as Parkinson's disease.