The pivotal role of artificial intelligence in enhancing experimental animal model research: A machine learning perspective

Ghosh, Anushka; Choudhary, Gajendra; Medhi, Bikash

doi:10.4103/ijp.ijp_81_24

Cited by 1 publication

(2 citation statements)

References 19 publications

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“…Social behavior in rodents is highly complex and dynamic, continually shifting as animals engage in back-and-forth interactions. Recent advances in machine learning and computational approaches for the analysis of social behavior have enabled a deeper, broader, and less biased approach towards understanding social behavior, including the ability to perform automated behavior classification and to identify behavioral patterns beyond what can be determined by the naked eye (Datta et al, 2019; Flavell et al, 2022; Ghosh et al, 2024; Kennedy, 2022; M. W. Mathis & Mathis, 2020; Pereira et al, 2020). To test whether SAUSI lends itself to modern computational approaches for postural tracking and analysis of behavior, we applied a variety of computational tools to videos obtained from our SAUSI assay (behavior displayed in Fig.…”

Section: Resultsmentioning

confidence: 99%

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SAUSI: an integrative assay for measuring social aversion and motivation

Grammer,

Valles,

Bowles

et al. 2024

Preprint

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Social anxiety is one of the most prevalent mental health disorders, though the underlying neurobiology is poorly understood. Progress in understanding the etiology of social anxiety has been hindered by the lack of comprehensive tools to assess social anxiety in model systems. Here, we created a new behavioral task – Selective Access to Unrestricted Social Interaction (SAUSI), which combines elements of social motivation, hesitancy, decision-making, and free interaction to enable the wholistic assessment of social anxiety-like behaviors in mice. Using this novel assay, we found that social isolation-induced social anxiety-like behaviors in female mice are largely driven by increases in social fear, social hesitancy, and altered ultrasonic vocalizations. Deep learning analyses were able to computationally identify a unique behavioral footprint underlying the state produced by social isolation, demonstrating the compatibility of modern computational approaches with SAUSI. Finally, we compared the results of SAUSI to traditionally social assays including the 3-chamber sociability assay and the resident intruder task. This revealed that behavioral changes induced by isolation were highly context dependent, and that while fragments of social anxiety measured in SAUSI were replicable across other tasks, a wholistic assessment was not obtainable from these alternative assays. Our findings debut a novel task for the behavioral toolbox – one which overcomes limitations of previous assays, allowing for both social choice as well as free interaction, and offers a new approach for assessing social anxiety in rodents.

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Section: Resultsmentioning

confidence: 99%

“…These tools not only reduce bias and increase efficiency, but also allow us to visualize behavioral and vocal changes in innovative and novel ways. Thus, the development of novel behavioral assays which lend themselves to these techniques is pivotal in a new age of validity, reproducibility, and translational meaning (Ghosh et al, 2024).…”

Section: Introductionmentioning

confidence: 99%