State-dependent olfactory processing in freely behaving mice.

Decreased responsiveness to sensory stimuli during sleep is presumably mediated via thalamic gating. Without an obligatory thalamic relay in the olfactory system, the anterior piriform cortex (APC) is suggested to be a gate in anesthetized states. However, olfactory processing in natural sleep states remains undetermined. Here, we simultaneously record local field potentials (LFPs) in hierarchical olfactory regions (olfactory bulb [OB], APC, and orbitofrontal cortex) while optogenetically activating olfactory sensory neurons, ensuring consistent peripheral inputs across states in behaving mice. Surprisingly, evoked LFPs in sleep states (both non-rapid eye movement [NREM] and rapid eye movement [REM]) are larger and contain greater gamma-band power and cross-region coherence (compared to wakefulness) throughout the olfactory pathway, suggesting the lack of a central gate. Single-unit recordings from the OB and APC reveal a higher percentage of responsive neurons during sleep with a higher incidence of suppressed firing. Additionally, nasal breathing is slower and shallower during sleep, suggesting a partial peripheral gating mechanism.

Machine learning-based clustering and classification of mouse behaviors via respiratory patterns.

Breathing is dynamically modulated by metabolic needs as well as by emotional states. Even though rodents are invaluable models for investigating the neural control of respiration, current literature lacks systematic characterization of breathing dynamics across a broad spectrum of rodent behaviors. Here we uncover a wide diversity in breathing patterns across spontaneous, attractive odor-, stress-, and fear-induced behaviors in mice. Direct recordings of intranasal pressure afford more detailed respiratory information than more traditional whole-body plethysmography. K-means clustering groups 11 well-defined behavioral states into four clusters with distinct key respiratory features. Furthermore, we implement RUSBoost (random undersampling boost) classification, a supervised machine learning model, and find that breathing patterns can separate these behaviors with an accuracy of 80%. Taken together, our findings highlight the tight relationship between breathing and behavior and the potential use of breathing patterns to aid in distinguishing similar behaviors and inform about their internal states.