DIY-NAMIC Behavior: A High-Throughput Method to Measure Complex Phenotypes in the Homecage

Abstract

Complex behavioral assessment is becoming increasingly necessary in order to comprehensively assess in vivo manipulations in rodent models. Using operant behavioral paradigms provides rich data sets allowing for the careful analysis of behavioral phenotypes. However, one major limitation in these studies is the expense and work-load that are required using traditional methods. The equipment for commercial operant boxes can be prohibitively expensive, and the daily experimenter effort and mouse costs required for these studies is extensive. Rodents are generally trained on task-specific paradigms for months, tested every day for 5-7 days per week. Additionally, appetitive paradigms usually require food restriction and are also commonly run in the non-active light phase of the rodent circadian rhythm. These limitations make operant behavioral testing especially difficult during adolescence, a time period of interest with regards to the development of adult-like phenotypes and a high-risk period for the development of neuropsychiatric disorders, including those which involve impulsive behavior. In order to address these issues, we developed an automated, inexpensive, open-source method which allows the implementation of most standard operant paradigms in the homecage of rodents in shorter time frames without food restriction, and with much less experimenter effort. All construction and code for the DIY Nautiyal Automated Modular Instrumental Conditioning (DIY-NAMIC) system are open source. We demonstrate their utility here by measuring impulsive behavior in a pharmacology experiment, as well as in adolescent mice. Significance statement Rigorous behavioral assessment is critical to understand the neural basis of neuropsychiatric disorders using animal models. Operant behavioral paradigms provide the ability to measure complex phenotypes, however, traditional methods generally require time-consuming daily training for many weeks. We designed, built, and tested an open-source automated homecage system for appetitive instrumental conditioning that enables testing in shorter timeframes with reduced experimenter effort.

Neuropathic injury drives a generalized negative affective state in mice

“…Namely, future studies that employ 'closed-economy' behavioral paradigms, in which mice have access to the device 24 hours a day, 7 days a week as their only source of food, will allow for more explicit control of circadian and satiety variables, and will allow for more complex behavioral tasks to assess cognitive changes in SNI mice. Moreover, closed-economy paradigms would allow for longitudinal measurements of behavior from baseline through the induction and stabilization of the chronic pain state following SNI with minimal experimenter intervention [7,30,48,67]. These longitudinal experiments would shed light on whether variability in performance emerges because of SNI, or if individual differences in performance exist prior to the injury and are exacerbated with the induction of chronic neuropathic pain.…”

FreiBox: A Versatile Open-Source Behavioral Setup for Investigating the Neuronal Correlates of Behavioral Flexibility via 1-Photon Imaging in Freely Moving Mice

“…Typical behavioral responses in freely moving animals are nose pokes (NPs) in specific chambers, visits in maze arms, presses on a specific lever, or movements of a joystick in a specific direction to obtain a reward ( Castañé et al, 2010 ; Parker et al, 2016 ; Izquierdo et al, 2017 ; Belsey et al, 2020 ; Lee et al, 2020 ; Mazziotti et al, 2020 ; Vassilev et al, 2022 ). For instance, Vassiliev et al (2022) recently developed a well documented and open-source setup to perform a Go/No-Go task.…”

Custom-Built Operant Conditioning Setup for Calcium Imaging and Cognitive Testing in Freely Moving Mice

“…Therefore, the interest in open-source platforms and tools is gaining increasing popularity ( Freeman, 2015 ; White et al, 2019 ). Many scientific laboratories now produce their own behavioral platforms, brain imaging systems and data analytic tools to address specific research questions ( Pineno, 2014 ; Freeman, 2015 ; Devarakonda et al, 2016 ; Kokras et al, 2017 ; O’Leary et al, 2018 ; Ribeiro et al, 2018 ; Erskine et al, 2019 ; Gurley, 2019 ; Buscher et al, 2020 ; Lee et al, 2020 ; Mazziotti et al, 2020 ; Tran et al, 2020 ; Vassilev et al, 2020 ; Matikainen-Ankney et al, 2021 ).…”