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

ABSTRACT: Summary 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 machi

SUBMITTER: Janke E 

PROVIDER: S-EPMC9720017 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature