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Developing a 3-choice serial reaction time task for examining neural and cognitive function in an equine model.

Abstract：

BACKGROUND:Large animal models of human neurological disorders are advantageous compared to rodent models due to their neuroanatomical complexity, longevity and their ability to be maintained in naturalised environments. Some large animal models spontaneously develop behaviours that closely resemble the symptoms of neural and psychiatric disorders. The horse is an example of this; the domestic form of this species consistently develops spontaneous stereotypic behaviours akin to the compulsive and impulsive behaviours observed in human neurological disorders such as Tourette's syndrome. The ability to non-invasively probe normal and abnormal equine brain function through cognitive testing may provide an extremely useful methodological tool to assess brain changes associated with certain human neurological and psychiatric conditions. NEW METHOD:An automated operant system with the ability to present visual and auditory stimuli as well as dispense salient food reward was developed. To validate the system, ten horses were trained and tested using a standard cognitive task (three choice serial reaction time task (3-CSRTT)). RESULTS:All animals achieved total learning criterion and performed six probe sessions. Learning criterion was met within 16.30±0.79 sessions over a three day period. During six probe sessions, level of performance was maintained at 80.67±0.57% (mean±SEM) accuracy. COMPARISON WITH EXISTING METHOD(S):This is the first mobile fully automated system developed to examine cognitive function in the horse. CONCLUSIONS:A fully-automated operant system for mobile cognitive function of a large animal model has been designed and validated. Horses pose an interesting complementary model to rodents for the examination of human neurological dysfunction.

journal_name

J Neurosci Methods

journal_title

Journal of neuroscience methods

authors

Roberts K,Hemmings AJ,McBride SD,Parker MO

doi

10.1016/j.jneumeth.2017.01.018

subject

Has Abstract

pub_date

2017-12-01 00:00:00

pages

45-52

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(17)30033-X

journal_volume

292

pub_type

杂志文章

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