Koki Mimura: Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan. mimura.koki@qst.go.jp. ORCID
Jumpei Matsumoto: Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, 930-8555, Japan.
Daichi Mochihashi: Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics, Tokyo, 190-9562, Japan.
Tomoaki Nakamura: Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Tokyo, 182-8585, Japan.
Hisao Nishijo: Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, 930-8555, Japan.
Makoto Higuchi: Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan.
Toshiyuki Hirabayashi: Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan.
Takafumi Minamimoto: Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan. minamimoto.takafumi@qst.go.jp. ORCID
Nonhuman primates (NHPs) exhibit complex and diverse behavior that typifies advanced cognitive function and social communication, but quantitative and systematical measure of this natural nonverbal processing has been a technical challenge. Specifically, a method is required to automatically segment time series of behavior into elemental motion motifs, much like finding meaningful words in character strings. Here, we propose a solution called SyntacticMotionParser (SMP), a general-purpose unsupervised behavior parsing algorithm using a nonparametric Bayesian model. Using three-dimensional posture-tracking data from NHPs, SMP automatically outputs an optimized sequence of latent motion motifs classified into the most likely number of states. When applied to behavioral datasets from common marmosets and rhesus monkeys, SMP outperformed conventional posture-clustering models and detected a set of behavioral ethograms from publicly available data. SMP also quantified and visualized the behavioral effects of chemogenetic neural manipulations. SMP thus has the potential to dramatically improve our understanding of natural NHP behavior in a variety of contexts.
References
Neuron. 2015 Dec 16;88(6):1121-1135
[PMID: 26687221]
