AutonoMouse: High throughput operant conditioning reveals progressive impairment with graded olfactory bulb lesions

Authors: Andrew Erskine, Thorsten Bus, Jan T. Herb, Andreas T. Schaefer (Francis Crick Institute; UCL; Max Planck Institute for Medical Research; University of Heidelberg). Journal: PLoS ONE 14(3):e0211571 (2019). DOI 10.1371/journal.pone.0211571. PMCID PMC6402634 (open access, CC BY).

Abstract

Operant conditioning is a crucial tool in neuroscience research for probing brain function. While molecular, anatomical and even physiological techniques have seen radical increases in throughput, efficiency, and reproducibility in recent years, behavioural tools have somewhat lagged behind. Here we present a fully automated, high-throughput system for self-initiated conditioning of up to 25 group-housed, radio-frequency identification (RFID) tagged mice over periods of several months and >106 trials. We validate this “AutonoMouse” system in a series of olfactory behavioural tasks and show that acquired data is comparable to previous semi-manual approaches. Furthermore, we use AutonoMouse to systematically probe the impact of graded olfactory bulb lesions on olfactory behaviour, demonstrating that while odour discrimination in general is robust to even most extensive disruptions, small olfactory bulb lesions already impair odour detection. Discrimination learning of similar mixtures as well as learning speed are in turn reliably impacted by medium lesion sizes. The modular nature and open-source design of AutonoMouse should allow for similar robust and systematic assessments across neuroscience research areas.

Key points for the wiki

• Fully automated, high-throughput system for self-initiated conditioning of up to 25 group-housed RFID-tagged mice over several months and >10^6 trials.
• Validated with olfactory behavioural tasks; data comparable to semi-manual approaches.
• Systematic lesion study: odour discrimination robust to extensive olfactory bulb lesions, but small lesions already impair odour detection; learning of similar mixtures and learning speed impaired by medium lesions.
• Modular, open-source design (construction guide and software manual included as supplementary material; data/code on Figshare).