High-precision monitoring of nasal pressure in freely moving mice provides state-specific respiratory features and vigilance state prediction. - Archive ouverte HAL Accéder directement au contenu
Poster De Conférence Année : 2022

High-precision monitoring of nasal pressure in freely moving mice provides state-specific respiratory features and vigilance state prediction.

Résumé

In olfaction, odor sampling is controlled by respiratory/sniffing behaviors, which determine the dynamics of odor stimulation. The temporal pattern of airflow in the nasal cavity is therefore a fundamental parameter to consider when studying brain processing of odorant stimuli. Increasing evidence indicates that the olfactory system uses a precise temporal code which requires the detection of sniff onsets with millisecond precision. Yet, it remains unclear whether other characteristics of the respiratory cycle waveforms (such as airflow direction and respiratory pauses) can impact olfactory coding. These considerations prompted us to develop a new method that allow recording nasal airflow in freely moving mice with an enhanced level of precision as compared to methods used previously such as thermocouples. We have adapted an approach which allows measuring variations in the air pressure within the nasal cavity of freely moving mice. It relies on light (<1g) pressure sensors that can be carried by small rodents and combined with in vivo chronic neuronal recordings. Our data provides significant improvement in precision (e.g., better access to amplitude variations, cycle onset and inhalation/exhalation timing) compared to classical thermocouple recordings. We combined nasal pressure monitoring with hippocampal recordings in both male and female mice from different strains and developed an analysis pipeline partially based on the Breathmetrics toolbox created by the Zelano Lab (Northwestern University). Based on these data, we provide an in-depth characterization of the respiratory signal across brain states (Wake, REM and Non-REM Sleep). The respiratory signal in each brain state is characterized by a specific proportion of each component (inhalation, exhalation, pauses) and specific cycle features (amplitude, durations⋯). Thanks to these specific features, we have built a classifier to predict brain states based on respiratory signal features. Overall, this new method of respiration monitoring could be useful for neuroscientists interested in olfactory coding in naturalistic (freely moving) conditions as well as those studying the impact of respiratory rhythms on cognition.
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Dates et versions

hal-04285060 , version 1 (14-11-2023)

Identifiants

  • HAL Id : hal-04285060 , version 1

Citer

Camille Miermon, Giulio Casali, Nicolas Chenouard, Geoffrey Terral, Tiphaine Dolique, et al.. High-precision monitoring of nasal pressure in freely moving mice provides state-specific respiratory features and vigilance state prediction.. 2022 Society for Neuroscience Meeting, Nov 2022, San Diego, United States. ⟨hal-04285060⟩

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