A mobile, high-throughput semi-automated system for testing cognition in large non-primate animal models of Huntington disease.

Abstract:

BACKGROUND:For reasons of cost and ethical concerns, models of neurodegenerative disorders such as Huntington disease (HD) are currently being developed in farm animals, as an alternative to non-human primates. Developing reliable methods of testing cognitive function is essential to determining the usefulness of such models. Nevertheless, cognitive testing of farm animal species presents a unique set of challenges. The primary aims of this study were to develop and validate a mobile operant system suitable for high throughput cognitive testing of sheep. NEW METHOD:We designed a semi-automated testing system with the capability of presenting stimuli (visual, auditory) and reward at six spatial locations. Fourteen normal sheep were used to validate the system using a two-choice visual discrimination task. Four stages of training devised to acclimatise animals to the system are also presented. RESULTS:All sheep progressed rapidly through the training stages, over eight sessions. All sheep learned the 2CVDT and performed at least one reversal stage. The mean number of trials the sheep took to reach criterion in the first acquisition learning was 13.9±1.5 and for the reversal learning was 19.1±1.8. COMPARISON WITH EXISTING METHOD(S):This is the first mobile semi-automated operant system developed for testing cognitive function in sheep. CONCLUSIONS:We have designed and validated an automated operant behavioural testing system suitable for high throughput cognitive testing in sheep and other medium-sized quadrupeds, such as pigs and dogs. Sheep performance in the two-choice visual discrimination task was very similar to that reported for non-human primates and strongly supports the use of farm animals as pre-clinical models for the study of neurodegenerative diseases.

journal_name

J Neurosci Methods

authors

McBride SD,Perentos N,Morton AJ

doi

10.1016/j.jneumeth.2015.08.025

subject

Has Abstract

pub_date

2016-05-30 00:00:00

pages

25-33

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(15)00316-7

journal_volume

265

pub_type

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