Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses

Chronic digoxin exposure causes hyperactivity, anxiolysis, and alters brain monoamine content in zebrafish (Danio rerio)

To investigate the effects of chronic exposure to the cardiotonic steroid digoxin on locomotor activity, anxiety, and brain tissue monoamine content in Zebrafish. In total 24 adult (3-5 months) wild-type experimentally naïve zebrafish (50 : 50 ratio of females to males) were housed in 4-L tanks, in groups of six animals per tank. Two μM Digoxin was maintained in half of the tanks for 7 days. The 'Novel tank test' was performed on day 7 and the animals were euthanized. Concentrations of dopamine, serotonin, and their metabolites were then quantified in brain tissue using HPLC-ED. Seven-day exposure to 2 μM water solution of digoxin caused robust hyperlocomotion and reduced anxiety-like behavior in adult zebrafish in the 'Novel tank test'. The treatment also evoked pronounced neurochemical responses in zebrafish, including increased whole-brain 3-methoxytyramine, reduced norepinephrine and serotonin, and unaltered dopamine, homovanillic acid or 5-hydroxyindoleacetic acid levels. Deficits in monoaminergic (dopaminergic, serotonergic, and noradrenergic) neurotransmission are a key pathogenetic factor for multiple neuropsychiatric and neurodegenerative diseases. Commonly used clinically to treat cardiac conditions, cardiotonic steroids can affect dopaminergic neurotransmission. Chronic exposure to digoxin evokes hyperactivity-like behavior accompanied by altered monoamine neurotransmission in zebrafish, which may be relevant to understanding the central nervous system side effects of cardiotonic steroids.

Anxiety in aquatics: Leveraging machine learning models to predict adult zebrafish behavior

Accurate analysis of anxiety behaviors in animal models is pivotal for advancing neuroscience research and drug discovery. This study compares the potential of DeepLabCut, ZebraLab, and machine learning models to analyze anxiety-related behaviors in adult zebrafish. Using a dataset comprising video recordings of unstressed and pre-stressed zebrafish, we extracted features such as total inactivity duration/immobility, time spent at the bottom, time spent at the top and turn angles (large and small). We observed that the data obtained using DeepLabCut and ZebraLab were highly correlated. Using this data, we annotated behaviors as anxious and not anxious and trained several machine learning models, including Logistic Regression, Decision Tree, K-Nearest Neighbours (KNN), Random Forests, Naive Bayes Classifiers, and Support Vector Machines (SVMs). The effectiveness of these machine learning models was validated and tested on independent datasets. We found that some machine learning models, such as Decision Tree and Random Forests, performed excellently to differentiate between anxious and non-anxious behavior, even in the control group, where the differences between subjects were more subtle. Our findings show that upcoming technologies, such as machine learning models, are able to effectively and accurately analyze anxiety behaviors in zebrafish and provide a cost-effective method to analyze animal behavior.

Deep learning dives: Predicting anxiety in zebrafish through novel tank assay analysis

Behavior is fundamental to neuroscience research, providing insights into the mechanisms underlying thoughts, actions and responses. Various model organisms, including mice, flies, and fish, are employed to understand these mechanisms. Zebrafish, in particular, serve as a valuable model for studying anxiety-like behavior, typically measured through the novel tank diving (NTD) assay. Traditional methods for analyzing NTD assays are either manually intensive or costly when using specialized software. To address these limitations, it is useful to develop methods for the automated analysis of zebrafish NTD assays using deep-learning models. In this study, we classified zebrafish based on their anxiety levels using DeepLabCut. Subsequently, based on a training dataset of image frames, we compared deep-learning models to identify the model best suited to classify zebrafish as anxious or non anxious and found that specific architectures, such as InceptionV3, are able to effectively perform this classification task. Our findings suggest that these deep learning models hold promise for automated behavioral analysis in zebrafish, offering an efficient and cost-effective alternative to traditional methods.

Acute Behavioral and Neurochemical Effects of Sulpiride in Adult Zebrafish

Affective and psychotic disorders are highly prevalent and severely debilitating mental illnesses that often remain untreated or treatment-resistant. Sulpiride is a common antipsychotic (neuroleptic) drug whose well-established additional (e.g., antidepressant) therapeutic effects call for further studies of a wider spectrum of its CNS effects. Here, we examined effects of acute 20-min exposure to sulpiride (50-200 mg/L) on anxiety- and depression-like behaviors, as well as on brain monoamines, in adult zebrafish (Danio rerio). Overall, sulpiride exerted overt anxiolytic-like effects in the novel tank test and showed tranquilizing-like effects in the zebrafish tail immobilization test, accompanied by lowered whole-brain dopamine and its elevated turnover, without affecting serotonin or norepinephrine levels and their turnover. Taken together, these findings support complex behavioral pharmacology of sulpiride in vivo and reconfirm high sensitivity of zebrafish-based screens to this and, likely, other related clinically active neuroleptics.

A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish

BACKGROUND

Although zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications have empowered automated image recognition and video-tracking to ensure more efficient behavioral testing.

NEW METHOD

Capitalizing on several AI tools that most recently became available, here we present a novel open-access AI-driven platform to analyze tracks of adult zebrafish collected from in vivo neuropharmacological experiments. For this, we trained the AI system to distinguish zebrafish behavioral patterns following systemic treatment with several well-studied psychoactive drugs - nicotine, caffeine and ethanol.

RESULTS

Experiment 1 showed the ability of the AI system to distinguish nicotine and caffeine with 75 % and ethanol with 88 % probability and high (81 %) accuracy following a post-training exposure to these drugs. Experiment 2 further validated our system with additional, previously unexposed compounds (cholinergic arecoline and varenicline, and serotonergic fluoxetine), used as positive and negative controls, respectively.

COMPARISON WITH EXISTING METHODS

The present study introduces a novel open-access AI-driven approach to analyze locomotor activity of adult zebrafish.

CONCLUSIONS

Taken together, these findings support the value of custom-made AI tools for unlocking full potential of zebrafish CNS drug research by monitoring, processing and interpreting the results of in vivo experiments.

Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories

Zebrafish are ideal model organisms for various fields of biological research, including genetics, neural transmission patterns, disease and drug testing, and heart disease studies, because of their unique ability to regenerate cardiac muscle. Tracking zebrafish trajectories is essential for understanding their behavior, physiological states, and disease associations. While 2D tracking methods are limited, 3D tracking provides more accurate descriptions of their movements, leading to a comprehensive understanding of their behavior. In this study, we used deep learning models to track the 3D movements of zebrafish. Videos were captured by two custom-made cameras, and 21,360 images were labeled for the dataset. The YOLOv7 model was trained using hyperparameter tuning, with the top- and side-view camera models trained using the v7x.pt and v7.pt weights, respectively, over 300 iterations with 10,680 data points each. The models achieved impressive results, with an accuracy of 98.7% and a recall of 98.1% based on the test set. The collected data were also used to generate dynamic 3D trajectories. Based on a test set with 3,632 3D coordinates, the final model detected 173.11% more coordinates than the initial model. Compared to the ground truth, the maximum and minimum errors decreased by 97.39% and 86.36%, respectively, and the average error decreased by 90.5%.This study presents a feasible 3D tracking method for zebrafish trajectories. The results can be used for further analysis of movement-related behavioral data, contributing to experimental research utilizing zebrafish.

Artificial Neural Network (ANN)-Based Pattern Recognition Approach Illustrates a Biphasic Behavioral Effect of Ethanol in Zebrafish: A High-Throughput Method for Animal Locomotor Analysis

Variations in stress responses between individuals are linked to factors ranging from stress coping styles to the sensitivity of neurotransmitter systems. Many anxiolytic compounds can increase stressor engagement through the modulation of neurotransmitter systems and are used to investigate stress response mechanisms. The effect of such modulation may vary in time depending on concentration or environment, but those effects are hard to dissect because of the slow transition. We investigated the temporal effect of ethanol and found that ethanol-treated individual zebrafish larvae showed altered behavior that is different between drug concentrations and decreases with time. We used an artificial neural network approach with a time-dependent method for analyzing long (90 min) experiments on zebrafish larvae and found that individuals from the 0.5% group begin to show locomotor activity corresponding to the control group starting from the 60th minute. The locomotor activity of individuals from the 2% group after the 80th minute is classified as the activity of individuals from the 1.5% group. Our method shows three clusters of different concentrations in comparison with two clusters, which were obtained with the usage of a statistical approach for analyzing just the speed of fish movements. In addition, we show that such changes are not explained by basic behavior statistics such as speed and are caused by shifts in locomotion patterns.

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.

Behavioral profile of adult zebrafish acutely exposed to a selective dopamine uptake inhibitor, GBR 12909

BACKGROUND

The dopamine transporter (DAT) is the main regulator of dopamine concentration in the extrasynaptic space. The pharmacological inhibition of the DAT results in a wide spectrum of behavioral manifestations, which have been identified so far in a limited number of species, mostly in rodents.

AIM

Here, we used another well-recognized model organism, the zebrafish (Danio rerio), to explore the behavioral effects of GBR 12909, a highly-affine selective DAT blocker.

METHODS

We evaluated zebrafish locomotion, novelty-related exploration, spatial cognition, and social phenotypes in the novel tank, habituation and shoaling tests, following acute 20-min water immersion in GBR 12909.

RESULTS

Our findings show hypolocomotion, anxiety-like state, and impaired spatial cognition in fish acutely treated with GBR 12909. This behavioral profile generally parallels that of the DAT knockout rodents and zebrafish, and it overlaps with behavioral effects of other DAT-inhibiting drugs of abuse, such as cocaine and D-amphetamine.

CONCLUSION

Collectively, our data support the utility of zebrafish in translational studies on DAT targeting neuropharmacology and strongly implicate DAT aberration as an important mechanisms involved in neurological and psychiatric diseases.

Current State of Modeling Human Psychiatric Disorders Using Zebrafish

Psychiatric disorders are highly prevalent brain pathologies that represent an urgent, unmet biomedical problem. Since reliable clinical diagnoses are essential for the treatment of psychiatric disorders, their animal models with robust, relevant behavioral and physiological endpoints become necessary. Zebrafish (Danio rerio) display well-defined, complex behaviors in major neurobehavioral domains which are evolutionarily conserved and strikingly parallel to those seen in rodents and humans. Although zebrafish are increasingly often used to model psychiatric disorders, there are also multiple challenges with such models as well. The field may therefore benefit from a balanced, disease-oriented discussion that considers the clinical prevalence, the pathological complexity, and societal importance of the disorders in question, and the extent of its detalization in zebrafish central nervous system (CNS) studies. Here, we critically discuss the use of zebrafish for modeling human psychiatric disorders in general, and highlight the topics for further in-depth consideration, in order to foster and (re)focus translational biological neuroscience research utilizing zebrafish. Recent developments in molecular biology research utilizing this model species have also been summarized here, collectively calling for a wider use of zebrafish in translational CNS disease modeling.

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

The mammalian target of rapamycin (mTOR) is an important molecular regulator of cell growth and proliferation. Brain mTOR activity plays a crucial role in synaptic plasticity, cell development, migration and proliferation, as well as memory storage, protein synthesis, autophagy, ion channel expression and axonal regeneration. Aberrant mTOR signaling causes a diverse group of neurological disorders, termed 'mTORopathies'. Typically arising from mutations within the mTOR signaling pathway, these disorders are characterized by cortical malformations and other neuromorphological abnormalities that usually co-occur with severe, often treatment-resistant, epilepsy. Here, we discuss recent advances and current challenges in developing experimental models of mTOR-dependent epilepsy and other related mTORopathies, including using zebrafish models for studying these disorders, as well as outline future directions of research in this field.

Towards Zebrafish Models of CNS Channelopathies

Channelopathies are a large group of systemic disorders whose pathogenesis is associated with dysfunctional ion channels. Aberrant transmembrane transport of K⁺, Na⁺, Ca²⁺ and Cl^- by these channels in the brain induces central nervous system (CNS) channelopathies, most commonly including epilepsy, but also migraine, as well as various movement and psychiatric disorders. Animal models are a useful tool for studying pathogenesis of a wide range of brain disorders, including channelopathies. Complementing multiple well-established rodent models, the zebrafish (Danio rerio) has become a popular translational model organism for neurobiology, psychopharmacology and toxicology research, and for probing mechanisms underlying CNS pathogenesis. Here, we discuss current prospects and challenges of developing genetic, pharmacological and other experimental models of major CNS channelopathies based on zebrafish.

Acute behavioral and Neurochemical Effects of Novel N-Benzyl-2-Phenylethylamine Derivatives in Adult Zebrafish

Hallucinogenic drugs potently affect brain and behavior and have also recently emerged as potentially promising agents in pharmacotherapy. Complementing laboratory rodents, the zebrafish (Danio rerio) is a powerful animal model organism for screening neuroactive drugs, including hallucinogens. Here, we test a battery of ten novel N-benzyl-2-phenylethylamine (NBPEA) derivatives with the 2,4- and 3,4-dimethoxy substitutions in the phenethylamine moiety and the -OCH₃, -OCF₃, -F, -Cl, and -Br substitutions in the ortho position of the phenyl ring of the N-benzyl moiety, assessing their acute behavioral and neurochemical effects in the adult zebrafish. Overall, substitutions in the Overall, substitutions in the N-benzyl moiety modulate locomotion, and substitutions in the phenethylamine moiety alter zebrafish anxiety-like behavior, also affecting the brain serotonin and/or dopamine turnover. The 24H-NBOMe(F) and 34H-NBOMe(F) treatment also reduced zebrafish despair-like behavior. Computational analyses of zebrafish behavioral data by artificial intelligence identified several distinct clusters for these agents, including anxiogenic/hypolocomotor (24H-NBF, 24H-NBOMe, and 34H-NBF), behaviorally inert (34H-NBBr, 34H-NBCl, and 34H-NBOMe), anxiogenic/hallucinogenic-like (24H-NBBr, 24H-NBCl, and 24H-NBOMe(F)), and anxiolytic/hallucinogenic-like (34H-NBOMe(F)) drugs. Our computational analyses also revealed phenotypic similarity of the behavioral activity of some NBPEAs to that of selected conventional serotonergic and antiglutamatergic hallucinogens. In silico functional molecular activity modeling further supported the overlap of the drug targets for NBPEAs tested here and the conventional serotonergic and antiglutamatergic hallucinogens. Overall, these findings suggest potent neuroactive properties of several novel synthetic NBPEAs, detected in a sensitive in vivo vertebrate model system, the zebrafish, raising the possibility of their potential clinical use and abuse.

MPTP-Treated Zebrafish Recapitulate 'Late-Stage' Parkinson's-like Cognitive Decline

The zebrafish is a promising model species in biomedical research, including neurotoxicology and neuroactive drug screening. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) evokes degeneration of dopaminergic neurons and is commonly used to model Parkinson's disease (PD) in laboratory animals, including zebrafish. However, cognitive phenotypes in MPTP-evoked experimental PD models remain poorly understood. Here, we established an LD50 (292 mg/kg) for intraperitoneal MPTP administration in adult zebrafish, and report impaired spatial working memory (poorer spontaneous alternation in the Y-maze) in a PD model utilizing fish treated with 200 µg of this agent. In addition to conventional behavioral analyses, we also employed artificial intelligence (AI)-based approaches to independently and without bias characterize MPTP effects on zebrafish behavior during the Y-maze test. These analyses yielded a distinct cluster for 200-μg MPTP (vs. other) groups, suggesting that high-dose MPTP produced distinct, computationally detectable patterns of zebrafish swimming. Collectively, these findings support MPTP treatment in adult zebrafish as a late-stage experimental PD model with overt cognitive phenotypes.