Klibaite Ugne, Li Tianqing, Aldarondo Diego, Akoad Jumana F, Ölveczky Bence P, Dunn Timothy W
Department of Organismic and Evolutionary Biology, Harvard University.
Department of Biomedical Engineering, Duke University.
bioRxiv. 2024 Sep 27:2024.09.27.615451. doi: 10.1101/2024.09.27.615451.
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.
社交互动是动物行为不可或缺的一部分。然而，我们缺乏能够严格定量描述它的工具，这限制了我们对其原理以及像自闭症这种扰乱社交互动的神经精神疾病的理解。在此，我们提出一种用于对自由互动动物的姿势动态和社交接触进行高分辨率三维跟踪的技术，利用三维几何推理、图神经网络和半监督学习解决具有挑战性的主体遮挡和部分分配问题。我们在相互作用的啮齿动物中收集了超过1.4亿个三维姿势，其中包括缺乏社交行为表型报道的新型单基因自闭症大鼠品系。使用一种新颖的多尺度嵌入方法，我们识别出了丰富多样的刻板动作、互动、同步性和身体接触。这种增强的表型分析揭示了自闭症模型以及对苯丙胺反应中一系列传统测量无法检测到的变化。我们的框架和大量互动库将极大地促进对社交行为及其神经生物学基础的研究。
