Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses

D.V. Bozhko, V.O. Myrov, S.M. Kolchanova, A.I. Polovian, G.K. Galumov, K.A. Demin, K.N. Zabegalov, T. Strekalova, M.S. De Abreu, E.V. Petersen, A.V. Kalueff*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Web of Science)

Abstract

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.
Original languageEnglish
Article number110405
Number of pages12
JournalProgress in Neuro-Psychopharmacology & Biological Psychiatry
Volume112
DOIs
Publication statusPublished - 10 Jan 2022

Keywords

  • ADULT ZEBRAFISH
  • ANXIETY
  • Artificial intelligence
  • BEHAVIOR
  • CANCER
  • CNS drug screening
  • DEEP
  • FRAMEWORK
  • LSD
  • Locomotion
  • MODEL
  • NEURAL-NETWORKS
  • Neural network
  • TOOLS
  • Zebrafish
  • SYSTEM

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