scholarly journals Early Detection of Apathetic Phenotypes in Huntington’s Disease Knock-in Mice Using Open Source Tools

2017 ◽  
Author(s):  
Shawn Minnig ◽  
Robert M. Bragg ◽  
Hardeep S. Tiwana ◽  
Wes T. Solem ◽  
William S. Hovander ◽  
...  

AbstractApathy is one of the most prevalent and progressive psychiatric symptom in Huntington’s disease (HD) patients. However, preclinical work in HD mouse models tend to focus on molecular and motor, rather than affective, phenotypes. Measuring behavior in mice often produces noisy data and requires large cohorts to detect phenotypic rescue with appropriate power. The operant equipment necessary for measuring affective phenotypes is typically expensive, proprietary to commercial entities, and bulky which can render adequately sized mouse cohorts as cost-prohibitive. Thus, we describe here a home-built open-source alternative to commercial hardware that is reliable, scalable, and reproducible. Using off-the-shelf hardware, we adapted and built several of the rodent operant buckets (ROBucket) designed to test HttQ111/+ mice for attention deficits in fixed ratio (FR) and progressive ratio (PR) tasks. We find that, despite normal performance in reward attainment in the FR task, HttQ111/+ mice exhibit reduced PR performance at 9-11 months of age, suggesting motivational deficits. We replicated this in two independent cohorts, which demonstrates the reliability and utility of both the apathetic phenotype, and these ROBuckets, for preclinical HD studies.

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Shawn Minnig ◽  
Robert M. Bragg ◽  
Hardeep S. Tiwana ◽  
Wes T. Solem ◽  
William S. Hovander ◽  
...  

2005 ◽  
Vol 1 ◽  
pp. S26-S26
Author(s):  
Jane S. Paulsen ◽  
Julie C. Stout ◽  
Elizabeth Aylward ◽  
Karl Kieburtz ◽  
Douglas Langbehn ◽  
...  

1980 ◽  
Vol 8 (4) ◽  
pp. 343-347 ◽  
Author(s):  
Harold L. Klawans ◽  
Christopher G. Goetz ◽  
Stuart Perlik

2010 ◽  
Vol 5 (1) ◽  
pp. 85-104 ◽  
Author(s):  
Jane S Paulsen

2016 ◽  
Author(s):  
Matthew Longley ◽  
Ethan L Willis ◽  
Cindy X Tay ◽  
Hao Chen

Increasingly complex data sets are needed to fully understand the complexity in behavior. Credit card sized single-board computers with multi-core CPUs are an attractive platform for designing devices capable of collecting multi-dimensional behavioral data. To demonstrate this idea, we created an easy to-use device for operant licking experiments and another device that records environmental variables. These systems collect data obtained from multiple input devices (e.g., radio frequency identification tag readers, touch and motion sensors, environmental sensors) and activate output devices (e.g., LED lights, syringe pumps) as needed. Data gathered from these devices can be automatically transferred to a remote server via a wireless network. We tested the operant device by training rats to obtain either sucrose or water under the control of a fixed ratio, a variable ratio, or a progressive ratio reinforcement schedule. The lick data demonstrated that the device has sufficient precision and time resolution to record the fast licking behavior of rats. Data from the environment monitoring device also showed reliable measurements. By providing the code and 3D design under an open source license, we believe these examples will stimulate innovation in behavioral studies. http://github.com/chen42/openbehavior.


PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2981 ◽  
Author(s):  
Matthew Longley ◽  
Ethan L. Willis ◽  
Cindy X. Tay ◽  
Hao Chen

We created an easy-to-use device for operant licking experiments and another device that records environmental variables. Both devices use the Raspberry Pi computer to obtain data from multiple input devices (e.g., radio frequency identification tag readers, touch and motion sensors, environmental sensors) and activate output devices (e.g., LED lights, syringe pumps) as needed. Data gathered from these devices are stored locally on the computer but can be automatically transferred to a remote server via a wireless network. We tested the operant device by training rats to obtain either sucrose or water under the control of a fixed ratio, a variable ratio, or a progressive ratio reinforcement schedule. The lick data demonstrated that the device has sufficient precision and time resolution to record the fast licking behavior of rats. Data from the environment monitoring device also showed reliable measurements. By providing the source code and 3D design under an open source license, we believe these examples will stimulate innovation in behavioral studies. The source code can be found at http://github.com/chen42/openbehavior.


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