ArticleiScience2022
Machine learning-based clustering and classification of mouse behaviors via respiratory patterns.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Respiratory pauses highlight sleep architecture in mice.Nature communications · 2026Article
- From Breath to Behavior: Respiratory Features Predict Visual Detection Performance.bioRxiv : the preprint server for biology · 2026Article
- Breathing Rate as a Marker for Noise-Induced Stress in Guinea Pigs.Brain sciences · 2025Article
- An update on spinal cord injury and diaphragm neuromotor control.Expert review of respiratory medicine · 2025Review
- Article
- Identification of eupneic breathing using machine learning.Journal of neurophysiology · 2024Article
- Respiratory Coupled Oscillations as a Mechanism of Attention to the Olfactory Environment.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Synaptic plasticity and roles of orexin in distinct domains of the olfactory tubercle.Frontiers in neural circuits · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.