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Mapping the landscape of social behavior. | LitMetric

AI Article Synopsis

  • Social interaction is crucial for understanding animal behavior and is particularly relevant in studying neuropsychiatric disorders like autism, but current methods lack detailed quantitative analysis.
  • A new technique has been developed for high-resolution 3D tracking of animal postures and social interactions, utilizing sophisticated technologies like graph neural networks to address complex tracking issues.
  • This research gathered extensive data from 140 million 3D postures in rodents, revealing detailed behavioral patterns and differences in autism models, ultimately providing deeper insights into social behaviors and their biological foundations.

Article Abstract

Social interaction is integral to animal behavior. However, we lack tools to describe it with quantitative rigor, limiting our understanding of its principles and neuropsychiatric disorders, like autism, that perturb it. Here, we present a technique for high-resolution 3D tracking of postural dynamics and social touch in freely interacting animals, solving the challenging subject occlusion and part assignment problems using 3D geometric reasoning, graph neural networks, and semi-supervised learning. We collected over 140 million 3D postures in interacting rodents, featuring new monogenic autism rat lines lacking reports of social behavioral phenotypes. Using a novel multi-scale embedding approach, we identified a rich landscape of stereotyped actions, interactions, synchrony, and body contact. This enhanced phenotyping revealed a spectrum of changes in autism models and in response to amphetamine that were inaccessible to conventional measurements. Our framework and large library of interactions will greatly facilitate studies of social behaviors and their neurobiological underpinnings.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11463623PMC
http://dx.doi.org/10.1101/2024.09.27.615451DOI Listing

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