Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan.
Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, 190-0014, Japan.
Commun Biol. 2024 Sep 3;7(1):1080. doi: 10.1038/s42003-024-06786-2.
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.
非人类灵长类动物（NHPs）表现出复杂多样的行为，这些行为典型地代表了高级认知功能和社会交流，但对这种自然的非语言处理进行定量和系统的测量一直是一个技术挑战。具体来说，需要一种方法将行为的时间序列自动分割成语素运动模式，就像在字符串中找到有意义的单词一样。在这里，我们提出了一种称为 SyntacticMotionParser（SMP）的解决方案，这是一种使用非参数贝叶斯模型的通用无监督行为解析算法。使用来自 NHPs 的三维姿势跟踪数据，SMP 自动输出优化的潜在运动模式序列，这些模式分类为最可能的状态数。当应用于普通狨猴和恒河猴的行为数据集时，SMP 优于传统的姿势聚类模型，并从公开可用的数据中检测到一组行为行为图谱。SMP 还量化和可视化了化学遗传神经操作的行为影响。因此，SMP 有可能在各种情况下极大地提高我们对自然 NHP 行为的理解。
