Zebrafish automatic monitoring system for conditioning and behavioral analysis

Abstract

Studies using zebrafish (Danio rerio) in neuro-behavioural research are growing. Measuring fish behavior by computational methods is one of the most efficient ways to avoid human bias in experimental analyses, extending them to various approaches. Sometimes, thorough analyses are difficult to do, as fish can behave unpredictably during an experimental strategy. However, the analyses can be implemented in an automated way, using an online strategy and video processing for a complete assessment of the zebrafish behavior, based on the detection and tracking of fish during an activity. Here, a fully automatic conditioning and detailed analysis of zebrafish behavior is presented. Microcontrolled components were used to control the delivery of visual and sound stimuli, in addition to the concise amounts of food after conditioned stimuli for adult zebrafish groups in a conventional tank. The images were captured and processed for automatic detection of the fish, and the training of the fish was done in two evaluation strategies: simple and complex. In simple conditioning, the zebrafish showed significant responses from the second attempt, learning that the conditioned stimulus was a predictor of food presentation in a specific space of the tank, where the food was dumped. When the fish were subjected to two stimuli for decision-making in the food reward, the zebrafish obtained better responses to red light stimuli in relation to vibration. The behavior change was clear in stimulated fish in relation to the control group, thus, the distances traveled and the speed were greater, while the polarization was lower in stimulated fish. This automated system allows for the conditioning and assessment of zebrafish behavior online, with greater stability in experiments, and in the analysis of the behavior of individual fish or fish schools, including learning and memory studies.

Speckle statistics as a tool to distinguish collective behaviors of Zebrafish shoals

“…Experimental data from ref. 22 for ⟨P ⟩ (squares), its standard deviation SD (circles), and the ratio ⟨P ⟩/SD (diamonds) are also included. These data indicate that the experimental setup used in ref.…”

“…In fish schools, relevant correlations are also developed in their velocity directions, promoting a synchronized motion of the group. To differentiate shoal and school collective behavior, the tendency of individuals to adopt the same orientation is usually quantified through group polarization defined as the magnitude of the average unit direction of all fish [13][14][15][16][17][18][19][20][21][22] .…”

“…The social behavior of zebrafish has been recently explored in several branches of biosciences. For example, learning and memory behavioral tests have been recently performed using visual and sound stimulous [22][23][24] , evidencing a lower polarization degree of stimulated fish.…”

Learning and memory formation in zebrafish: Protein dynamics and molecular tools

“…There have been developments in designing learning paradigms that are more appropriate for high-throughput screens ( Stewart et al, 2015 ). For example, automated systems for imaging, tracking, and analyzing dozens of larvae simultaneously ( Figure 1B ) ( Ahmed et al, 2010 ; Wolman et al, 2011 ; Doyle et al, 2016 ; Randlett et al, 2019 ; Barreiros et al, 2021 ); paradigms that can be performed with multiple adult fish at the same time ( Kareklas et al, 2018 ; Samaras and Pavlidis, 2020 ; Barreiros et al, 2021 ); and relatively short paradigms that do not last for more than 2 or 3 days ( Hinz et al, 2013 ; Lucon-Xiccato and Dadda, 2014 ). Efforts should be made to ensure that fish are habituated properly to the paradigm setup and show no signs of stress or anxiety, especially for paradigms that use individual zebrafish ( Gerlai, 2016 ).…”

Developing Novel Experimental Models of m-TORopathic Epilepsy and Related Neuropathologies: Translational Insights from Zebrafish

“…Furthermore, the development of information technology and its application to zebrafish behavioral phenotypes [ 100 , 101 ] may offer new opportunities for unbiased characterization of seizures in both larvae and adult fish. For example, simultaneous recording of fish behavior with two cameras enables reconstructing patterns of fish locomotion in 3D [ 102 ] and, if combined with machine learning protocols (e.g., [ 101 ]), may promote automatic recognition of various types of epilepsy-related behaviors in fish by artificial neural networks.…”