Impaired refinement of kinematic variability in Huntington disease mice on an automated home-cage forelimb motor task

Abstract

AbstractThe effective development of novel therapies in mouse models of neurological disorders relies on behavioural assessments that provide accurate read-outs of neuronal dysfunction and/or degeneration. We designed an automated behavioural testing system (‘PiPaw’) which integrates an operant lever-pulling task directly into the mouse home-cage. This task is accessible to group-housed mice 24-hours per day, enabling high-throughput longitudinal analysis of forelimb motor learning. Moreover, this design eliminates the need for exposure to novel environments and minimizes experimenter interaction, significantly reducing two of the largest stressors associated with animal behaviour. Mice improved their performance of this task over one week of testing by reducing inter-trial variability of reward-related kinematic parameters (pull amplitude or peak velocity). In addition, mice displayed short-term improvements in reward rate, and a concomitant decrease in movement variability, over the course of brief (<10 minutes) bouts of task engagement. We used this system to assess motor learning in mouse models of the inherited neurodegenerative disorder, Huntington disease (HD). Despite having no baseline differences in task performance, Q175-FDN HD mice were unable to modulate the variability of their movements in order to increase reward on either short or long timescales. Task training was associated with a decrease in the amplitude of spontaneous excitatory activity recorded from striatal medium spiny neurons in the hemisphere contralateral to the trained forelimb in wildtype mice; however, no such changes were observed in Q175-FDN mice. This behavioural screening platform should prove useful for preclinical drug trials towards improved treatments in HD and other neurological disorders.Significance StatementIn order to develop effective therapies for neurological disorders such as Huntington disease (HD), it’s important to be able to accurately and reliably assess the behaviour of mouse models of these conditions. Moreover, these behavioural assessments should provide an accurate readout of underlying neuronal dysfunction and/or degeneration. In this paper, we employed an automated behavioural testing system to assess motor learning in mice within their home-cage. Using this system, we were able to study motor abnormalities in HD mice with an unprecedented level of detail, and identified a specific behavioural deficit associated with an underlying impairment in striatal neuronal plasticity. These results validate the usefulness of this system for assessing behaviour in mouse models of HD and other neurological disorders.