期刊
JOURNAL OF NEUROPHYSIOLOGY
卷 119, 期 1, 页码 337-346出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00115.2017
关键词
automation; behavior; motor cortex; RFID; stroke
资金
- Canadian Institutes of Health Research (CIHR) Foundation Grant [MOP-111009]
- Foundation Leducq
- Heart and Stroke Foundation of Canada
- Canadian Neurophotonics Platform
- Canadian Partnership for Stroke Recovery expansion grant
- CIHR postdoctoral fellowship
- Canadian Partnership for Stroke Recovery
Skilled forelimb function in mice is traditionally studied through behavioral paradigms that require extensive training by investigators and are limited by the number of trials individual animals are able to perform within a supervised session. We developed a skilled lever positioning task that mice can perform within their home cage. The task requires mice to use their forelimb to precisely hold a lever mounted on a rotary encoder within a rewarded position to dispense a water reward. A Raspberry Pi microcomputer is used to record lever position during trials and to control task parameters, thus making this low-footprint apparatus ideal for use within animal housing facilities. Custom Python software automatically increments task difficulty by requiring a longer hold duration, or a more accurate hold position, to dispense a reward. The performance of individual animals within group-housed mice is tracked through radio-frequency identification implants, and data stored on the microcomputer may be accessed remotely through an active internet connection. Mice continuously engage in the task for over 2.5 mo and perform similar to 500 trials/24 h. Mice required similar to 15,000 trials to learn to hold the lever within a 10 degrees range for 1.5 s and were able to further refine movement accuracy by limiting their error to a 5 degrees range within each trial. These results demonstrate the feasibility of autonomously training group-housed mice on a forelimb motor task. This paradigm may be used in the future to assess functional recovery after injury or cortical reorganization induced by self-directed motor learning. NEW & NOTEWORTHY We developed a low-cost system for fully autonomous training of group-housed mice on a forelimb motor task. We demonstrate the feasibility of tracking both end-point, as well as kinematic performance of individual mice, with each performing thousands of trials over 2.5 mo. The task is run and controlled by a Raspberry Pi microcomputer, which allows for cages to be monitored remotely through an active internet connection.
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