Establishing an AI-based evaluation system that quantifies social/pathophysiological behaviors of common marmosets

Abstract

Nonhuman primates (NHPs) are indispensable animal models by virtue of the continuity of behavioral repertoires across primates, including humans. However, behavioral assessment at the laboratory level has so far been limited. By applying multiple deep neural networks trained with large-scale datasets, we established an evaluation system that could reconstruct and estimate three-dimensional (3D) poses of common marmosets, a small NHP that is suitable for analyzing complex natural behaviors in laboratory setups. We further developed downstream analytic methodologies to quantify a variety of behavioral parameters beyond simple motion kinematics, such as social interactions and the internal state behind actions, obtained solely from 3D pose data. Moreover, a fully unsupervised approach enabled us to detect progressively-appearing symptomatic behaviors over a year in a Parkinsons disease model. The high-throughput and versatile nature of our analytic pipeline will open a new avenue for neuroscience research dealing with big-data analyses of social/pathophysiological behaviors in NHPs.