Measuring Zebrafish Turning Rate

Exploring emotional contagion in zebrafish: A virtual-demonstrator study of positive and negative emotions

Emotional contagion, the transmission of emotions within a group, has been extensively studied in mammals but remains largely unexplored in fish. This study aims to investigate whether emotional contagion, specifically in terms of low and high anxiety levels, can be evoked in zebrafish. This freshwater species has been gaining momentum due to its high genetic homology to humans and complex behavioral repertoire, making it well-suited for exploring social behavior. Our hypothesis posits that zebrafish have the ability to transmit positive and negative emotions to one another through visual cues only and that this transmission is robust over time. To test this, we employed a virtual demonstrator fish approach, where videos of zebrafish exhibiting either high or low geotactic behavior were shown to live zebrafish. Geotaxis, the tendency of a fish to spend more time at the bottom of the tank, is a sensitive measure of anxiety, with high geotactic behavior corresponding to high anxiety levels and vice versa. Our findings indicate that the virtual demonstrator successfully transmitted emotional states to the live fish, as evidenced by changes in the time spent at the bottom of the tank, linear acceleration, and fast-turning maneuvers, metrics that quantify anxiety-like behaviors such as geotaxis and erratic movements. Additionally, we conducted a causal analysis using a transfer entropy approach, revealing a significant flow of information from the virtual demonstrator fish to the live fish, indicating the efficacy and potential of this approach in studying emotional contagion. This study provides additional empirical evidence of how visual cues alone from a virtual demonstrator exhibiting high or low anxious behavior can elicit similar behavioral states in bystander fish, highlighting the potential for emotional contagion beyond mammalian and avian models.

Modeling zebrafish geotaxis as a feedback control process

Developing mathematical models of the feedback control process underlying animal behavior is of critical importance to understand their interactions with the environment and emotional responses. For instance, fish geotaxis (the tendency to swim at the bottom of the tank) is known to be a highly sensitive measure of anxiety, but how and why animals tend to display such a complex response is yet to be fully clarified. Leveraging the theory of stochastic differential equations, we develop a data-driven model of geotaxis in the form of a feedback control loop where fish use information about the hydrostatic pressure to dive towards the bottom of the tank. The proposed framework extends open-loop models by incorporating a simple, yet effective, control mechanism to explain geotaxis. We focus on the zebrafish animal model, which is a species of choice in the study of anxiety disorders. We calibrate the model using available experimental data on acute ethanol treatment of adult zebrafish, and demonstrate its effectiveness across a wide range of comparisons between theoretical predictions and empirical observations.

Model-based feedback control of live zebrafish behavior via interaction with a robotic replica

The possibility of regulating the behavior of live animals using biologically-inspired robots has attracted the interest of biologists and engineers for over twenty-five years. From early work on insects to recent endeavors on mammals, we have witnessed fascinating applications that have pushed forward our understanding of animal behavior along new directions. Despite significant progress, most of the research has focused on open-loop control systems, in which robots execute predetermined actions, independent of the animal behavior. We integrate mathematical modeling of social behavior toward the design of realistic feedback laws for robots to interact with a live animal. In particular, we leverage recent advancements in data-driven modeling of zebrafish behavior. Ultimately, we establish a novel robotic platform that allows real-time actuation of a biologically-inspired 3D-printed zebrafish replica to implement model-based control of animal behavior. We demonstrate our approach through a series of experiments, designed to elucidate the appraisal of the replica by live subjects with respect to conspecifics and to quantify the biological value of closed-loop control.

Design and development of a robotic predator as a stimulus in conditioned place aversion for the study of the effect of ethanol and citalopram in zebrafish

Zebrafish are becoming a species of choice in psychopharmacology, laying a promising path to refined pharmacological manipulations and high-throughput behavioral phenotyping. The field of robotics has the potential to accelerate progress along this path, by offering unprecedented means for the design and development of accurate and reliable experimental stimuli. In this work, we demonstrate, for the first time, the integration of robotic predators in place conditioning experiments. We hypothesized zebrafish to be capable of forming a spatial association under a simulated predation risk. We repeatedly exposed experimental subjects to a robotic heron impacting the water surface and then evaluated their spatial avoidance within the experimental tank in a subsequent predator-free test session. To pharmacologically validate the paradigm, we tested zebrafish in drug-free conditions (control groups) or in response to three different concentrations of citalopram (30, 50, and 100 mg/L) and ethanol (0.25, 0.50, and 1.00%). Experimental data indicate that, when tested in the absence of the conditioning stimulus, zebrafish displayed a marked preference for the bottom of the test tank, that is, the farthest location from the simulated attacks by the robotic heron. This conditioned geotaxis was reduced by the administration of citalopram in a linear dose-response curve and ethanol at the low concentration. Ultimately, our data demonstrate that robotic stimuli may represent valid conditioning tools and, thereby, aid the field of zebrafish psychopharmacology.

Data-driven modeling of zebrafish behavioral response to acute caffeine administration

Over the last thirty years, we have witnessed a dramatic rise in the use of zebrafish in preclinical research. Every year, more than 5000 technical papers are published about zebrafish, many of them seeking to explain the underpinnings of anxiety through animal testing. In-silico experiments could significantly contribute to zebrafish research and welfare, by offering new means to support the 3Rs principles of replacement, reduction, and refinement. Here, we propose a data-driven modeling framework to predict the anxiety-related behavioral response of zebrafish to acute caffeine administration. The modeling framework unfolds along a two-time-scale dichotomy to capture freezing behavior along a slow temporal scale and burst-and-coast locomotion at a fast time-scale. Anchored in the theory of Markov chains and stochastic differential equations, we demonstrate a parsimonious, yet robust, modeling framework to accurately simulate experimental observations of zebrafish treated at different caffeine concentrations. Our results complement recent modeling efforts, laying the foundations for conducting in-silico experiments in zebrafish behavioral pharmacology.

Comparison between two- and three-dimensional scoring of zebrafish response to psychoactive drugs: identifying when three-dimensional analysis is needed

Zebrafish (Danio rerio) have recently emerged as a valuable laboratory species in the field of behavioral pharmacology, where they afford rapid and precise high-throughput drug screening. Although the behavioral repertoire of this species manifests along three-dimensional (3D), most of the efforts in behavioral pharmacology rely on two-dimensional (2D) projections acquired from a single overhead or front camera. We recently showed that, compared to a 3D scoring approach, 2D analyses could lead to inaccurate claims regarding individual and social behavior of drug-free experimental subjects. Here, we examined whether this conclusion extended to the field of behavioral pharmacology by phenotyping adult zebrafish, acutely exposed to citalopram (30, 50, and 100 mg/L) or ethanol (0.25%, 0.50%, and 1.00%), in the novel tank diving test over a 6-min experimental session. We observed that both compounds modulated the time course of general locomotion and anxiety-related profiles, the latter being represented by specific behaviors (erratic movements and freezing) and avoidance of anxiety-eliciting areas of the test tank (top half and distance from the side walls). We observed that 2D projections of 3D trajectories (ground truth data) may introduce a source of unwanted variation in zebrafish behavioral phenotyping. Predictably, both 2D views underestimate absolute levels of general locomotion. Additionally, while data obtained from a camera positioned on top of the experimental tank are similar to those obtained from a 3D reconstruction, 2D front view data yield false negative findings.

Argus: An open-source and flexible software application for automated quantification of behavior during social interaction in adult zebrafish

Zebrafish show great potential for behavioral neuroscience. Promising lines of research, however, require the development and validation of software tools that will allow automated and cost-effective behavioral analysis. Building on our previous work with the RealFishTracker (in-house-developed tracking system), we present Argus, a data extraction and analysis tool built in the open-source R language for behavioral researchers without any expertise in R. Argus includes a new, user-friendly, and efficient graphical user interface, instead of a command-line interface, and offers simplicity and flexibility in measuring complex zebrafish behavior through customizable parameters. In this article, we compare Argus with Noldus EthoVision and Noldus The Observer, to validate this new system. All three software applications were originally designed to quantify the behavior of a single subject. We first also performed an analysis of the movement of individual fish and compared the performance of the three software applications. Next we computed and quantified the behavioral variables that characterize dyadic interactions between zebrafish. We found that Argus and EthoVision extract similar absolute values and patterns of changes in these values for several behavioral measures, including speed, freezing, erratic movement, and interindividual distance. In contrast, the manual coding of behavior in The Observer showed weaker correlations with the two tracking methods (EthoVision and Argus). Thus, Argus is a novel, cost-effective, and customizable method for the analysis of adult zebrafish behavior that may be utilized for the behavioral quantification of both single and dyadic interacting subjects, but further sophistication will be needed for the proper identification of complex motor patterns, measures that a human observers can easily detect.

Information theory and robotics meet to study predator-prey interactions

Transfer entropy holds promise to advance our understanding of animal behavior, by affording the identification of causal relationships that underlie animal interactions. A critical step toward the reliable implementation of this powerful information-theoretic concept entails the design of experiments in which causal relationships could be systematically controlled. Here, we put forward a robotics-based experimental approach to test the validity of transfer entropy in the study of predator-prey interactions. We investigate the behavioral response of zebrafish to a fear-evoking robotic stimulus, designed after the morpho-physiology of the red tiger oscar and actuated along preprogrammed trajectories. From the time series of the positions of the zebrafish and the robotic stimulus, we demonstrate that transfer entropy correctly identifies the influence of the stimulus on the focal subject. Building on this evidence, we apply transfer entropy to study the interactions between zebrafish and a live red tiger oscar. The analysis of transfer entropy reveals a change in the direction of the information flow, suggesting a mutual influence between the predator and the prey, where the predator adapts its strategy as a function of the movement of the prey, which, in turn, adjusts its escape as a function of the predator motion. Through the integration of information theory and robotics, this study posits a new approach to study predator-prey interactions in freshwater fish.

Model-free information-theoretic approach to infer leadership in pairs of zebrafish

Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.

Effect of acute ethanol administration on zebrafish tail-beat motion

Zebrafish is becoming a species of choice in neurobiological and behavioral studies of alcohol-related disorders. In these efforts, the activity of adult zebrafish is typically quantified using indirect activity measures that are either scored manually or identified automatically from the fish trajectory. The analysis of such activity measures has produced important insight into the effect of acute ethanol exposure on individual and social behavior of this vertebrate species. Here, we leverage a recently developed tracking algorithm that reconstructs fish body shape to investigate the effect of acute ethanol administration on zebrafish tail-beat motion in terms of amplitude and frequency. Our results demonstrate a significant effect of ethanol on the tail-beat amplitude as well as the tail-beat frequency, both of which were found to robustly decrease for high ethanol concentrations. Such a direct measurement of zebrafish motor functions is in agreement with evidence based on indirect activity measures, offering a complementary perspective in behavioral screening.