Avoidance learning procedures involve subjects learning that certain aversive events are predicted by specific environmental stimuli.
In the following zebrafish version of the task, fish must learn that a specified visual cue (e.g., blue light) predicts an aversive stimulus (e.g., mild electric shock) (Brock et al., 2017; Kenney et al., 2016). This type of task is a robust method which assesses associative learning and memory, and it is reported to be impaired in many psychiatric disorders, such as anxiety disorders and schizophrenia and in neurodegenerative disorders, such as Alzheimer’s disease.
Experimental set up
The Zantiks AD unit is used for avoidance learning in zebrafish. The dividing inserts are used to create four separate testing compartments for tracking four fish in one unit (see image A below).
Metal “shock-plate” inserts are used to deliver the unconditioned stimulus (US+), a mild electric shock, through the water. The plates are placed at both ends of the tank and are designed to be present in each of the 4 arenas (as shown in images A and B below).
The visual stimuli are presented from the integrated screen below the testing tank (see image below depicting green and blue colours on the screen). The stimuli can be comprised of colour, shapes, stripes, or bitmap images.
Setting up the AD unit for a Pavlovian avoidance learning assay, including arena and shock plate set up and an introduction to a test script outlines how you can lay out the script with the four different periods, habituation, baseline, shock, probe.
Fish are initially habituated to the tank, and then tracked to establish baseline preference for the environmental stimuli. Both stimuli are presented for baseline measures, the screen below the tank is split to display green and blue colour stimuli.
The conditioned stimulus, an all blue screen, is paired with a mild electric shock, followed by the presentation of the non-conditioned stimulus, the green screen. Stimulus preference is then measured in a probe period following conditioning when both green and blue colour stimuli are presented on the screen as illustrated below.
The screen below the AD tank will display green and blue colour stimuli during the baseline and probe periods, as illustrated in this image captured during a study.
Results / data output
The conditioned stimulus preference scores are the main behavioural endpoint analysed in this task. Preference towards the CS is determined in the same manner for both baseline and probe calculations. The proportion of time spent in the vicinity of the CS is calculated for the preference as: Total time in CS / (Time in CS + Time in non-CS).
A decrease in the time spent in the CS can be interpreted that the fish has learned to avoid the environmental stimulus present during the electric shock conditioning. A learned association between the environmental stimulus and the electric shock results in the fish spending less time in that environmental stimulus. The Zantiks AD system can automatically track and provide data analysis on this variable.
The CS preference scores are frequently analysed with independent samples t-tests, one-way ANOVAs or two-way repeated measures ANOVAs, depending on the number of factors.
Results data file
The short experimental test illustrated below, demonstrates the kind of data that you can collect in real time. In this demo two fake fish, singly housed in two arenas, swim over two different coloured zones. You will note that fish 1 (in Arena 1) prefers the green zone.
Two fake fish in a demo experiment conducted in the AD unit illustrating how data can be collected
The zanscript for this experiment included commands to collect data on the movement of each of the two fake fish in two different coloured zones in their arenas, during two intervals (periods), and detailing the distance travelled (measured in pixels) and time spent (in seconds) in each zone as well as counting the number of times the fish entered each zone.
This .csv file illustrates how the data can be presented in real time. The screenshot below shows how the data in the .csv file imports into a spreadsheet, in this case Numbers.