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IOS Press, Journal of Alzheimer's Disease, 2(28), p. 459-469

DOI: 10.3233/jad-2011-110704

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Evaluation of color preference in zebrafish for learning and memory

This paper is available in a repository.
This paper is available in a repository.

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Data provided by SHERPA/RoMEO

Abstract

There is growing interest in using zebrafish (Danio rerio) as a model of neurodegenerative disorders such as Alzheimer’s disease. A zebrafish model of tauopathies has recently been developed and characterized in terms of presence of the pathological hallmarks (i.e., neurofibrillary tangles and cell death). However, it is also necessary to validate these models for function by assessing learning and memory. The majority of tools to assess memory and learning in animal models involve visual stimuli, including color preference. The color preference of zebrafish has received little attention. To validate zebrafish as a model for color-associated-learning and memory, it is necessary to evaluate its natural preferences or any pre-existing biases towards specific colors. In the present study, we have used four different colors (red, yellow, green, and blue) to test natural color preferences of the zebrafish using two procedures: Place preference and T-maze. Results from both experiments indicate a strong aversion toward blue color relative to all other colors (red, yellow, and green) when tested in combinations. No preferences or biases were found among reds, yellows, and greens in the place preference procedure. However, red and green were equally preferred and both were preferred over yellow by zebrafish in the T-maze procedure. The results from the present study show a strong aversion towards blue color compared to red, green, and yellow, with yellow being less preferred relative to red and green. The findings from this study may underpin any further designing of color-based learning and memory paradigms or experiments involving aversion, anxiety, or fear in the zebrafish. ; Avdesh Avdesh, Mathew T. Martin-Iverson, Alinda Mondal, Mengqi Chen, Sreten Askraba, Newman Morgan, Michael Lardelli and David M. Groth