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.

Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses.

Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.

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.

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.

BCNNM: A Framework for in silico Neural Tissue Development Modeling.

Cerebral ("brain") organoids are high-fidelity in vitro cellular models of the developing brain, which makes them one of the go-to methods to study isolated processes of tissue organization and its electrophysiological properties, allowing to collect invaluable data for in silico modeling neurodevelopmental processes. Complex computer models of biological systems supplement in vivo and in vitro experimentation and allow researchers to look at things that no laboratory study has access to, due to either technological or ethical limitations. In this paper, we present the Biological Cellular Neural Network Modeling (BCNNM) framework designed for building dynamic spatial models of neural tissue organization and basic stimulus dynamics. The BCNNM uses a convenient predicate description of sequences of biochemical reactions and can be used to run complex models of multi-layer neural network formation from a single initial stem cell. It involves processes such as proliferation of precursor cells and their differentiation into mature cell types, cell migration, axon and dendritic tree formation, axon pathfinding and synaptogenesis. The experiment described in this article demonstrates a creation of an in silico cerebral organoid-like structure, constituted of up to 1 million cells, which differentiate and self-organize into an interconnected system with four layers, where the spatial arrangement of layers and cells are consistent with the values of analogous parameters obtained from research on living tissues. Our in silico organoid contains axons and millions of synapses within and between the layers, and it comprises neurons with high density of connections (more than 10). In sum, the BCNNM is an easy-to-use and powerful framework for simulations of neural tissue development that provides a convenient way to design a variety of tractable in silico experiments.