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Abstract <p>Artificial intelligence (AI) is extensively used in biomedicinefor big data analysis. In modern neurobehavioral research, AI helpsmodel affective disorders and other pathological conditions of thecentral nervous system (CNS), including autism, neurodegenerativeand neurological diseases. AI-based approaches are also used forhigh-throughput preclinical screening of drugs, phenotyping of genetic mutants,analysis of social behavior, and assessment of individual motorpatterns. Recently, AI has been applied to assess behavior of zebrafish(<i>Danio rerio</i>) and other modelorganisms (mice, rats, <i>C. elegans</i>, Drosophila),which is particularly important for identifying common evolutionarilyconservative neurobiological mechanisms and phenotypes. Overall,AI markedly expands the potential of neurobiological research, enrichingthe methodological base and accelerating preclinical research andthe development of innovative therapeutic approaches, as well asidentifying novel, previously unknown phenotypes. Here, we discussmounting evidence of AI application in modern neurobehavioral research, andcritically evaluate the existing problems, challenges, and prospectsin this field.</p>

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Using Artificial Intelligence Systems in Modern Neurobehavioral Research

  • 期刊论文

  • 发表日期：

    2025年12月19日

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  • K. V. Apukhtin,
  • A. E. Zolotova,
  • A. O. Leunenko,
  • V. N. Perfilova,
  • A. V. Kalueff

摘要

Abstract

Artificial intelligence (AI) is extensively used in biomedicinefor big data analysis. In modern neurobehavioral research, AI helpsmodel affective disorders and other pathological conditions of thecentral nervous system (CNS), including autism, neurodegenerativeand neurological diseases. AI-based approaches are also used forhigh-throughput preclinical screening of drugs, phenotyping of genetic mutants,analysis of social behavior, and assessment of individual motorpatterns. Recently, AI has been applied to assess behavior of zebrafish(Danio rerio) and other modelorganisms (mice, rats, C. elegans, Drosophila),which is particularly important for identifying common evolutionarilyconservative neurobiological mechanisms and phenotypes. Overall,AI markedly expands the potential of neurobiological research, enrichingthe methodological base and accelerating preclinical research andthe development of innovative therapeutic approaches, as well asidentifying novel, previously unknown phenotypes. Here, we discussmounting evidence of AI application in modern neurobehavioral research, andcritically evaluate the existing problems, challenges, and prospectsin this field.