Atomoxetine reduces anticipatory responding in a 5-choice serial reaction time task for adult zebrafish
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Deficits in impulse control are related to a number of psychiatric diagnoses, including attention deficit hyperactivity disorder, addiction, and pathological gambling. Despite increases in our knowledge about the underlying neurochemical and neuroanatomical correlates, understanding of the molecular and cellular mechanisms is less well established. Understanding these mechanisms is essential in order to move towards individualized treatment programs and increase efficacy of interventions. Zebrafish are a very useful vertebrate model for exploring molecular processes underlying disease owing to their small size and genetic tractability. Their utility in terms of behavioral neuroscience, however, hinges on the validation and publication of reliable assays with adequate translational relevance. Here, we report an initial pharmacological validation of a fully automated zebrafish version of the commonly used five-choice serial reaction time task using a variable interval pre-stimulus interval. We found that atomoxetine reduced anticipatory responses (0.6 mg/kg), whereas a high-dose (4 mg/kg) methylphenidate increased anticipatory responses and the number of trials completed in a session. On the basis of these results, we argue that similar neurochemical processes in fish as in mammals may control impulsivity, as operationally defined by anticipatory responses on a continuous performance task such as this, making zebrafish potentially a good model for exploring the molecular basis of impulse control disorders and for first-round drug screening.
KeywordsFive-choice serial reaction time task Zebrafish Impulsivity Addiction ADHD Atomoxetine Methylphenidate
This research was funded by project grant G1000053 from the National Center for the Replacement, Reduction and Refinement of animals in research (NC3Rs; UK) and by the Medical Research Council (MRC; UK). CHB is a Royal Society (UK) Industrial Research Fellow. We acknowledge the contributions of Dennis Ife, Jun Ma and Chris Straw of the School of Engineering and Materials Science at Queen Mary University of London for building and engineering the automated testing arena, and Dr Fabrizio Smeraldi (Electronic Engineering and Computer Science) and Mahesh Pancholi (School of Biological and Chemical Sciences) for writing the visual tracking programming. We also thank the two anonymous reviewers for their helpful and constructive comments on earlier versions of this manuscript.
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