瑞士苏黎世联邦理工学院Johannes Bohacek研究组，开发出行为流分析揭示了潜在表型。2024年11月12日，国际知名学术期刊《自然—方法学》在线发表了这一成果。

研究人员提出了一种新的工作流程，通过捕捉每只动物的行为流，基于所有观察到的群集间过渡生成一个单一的度量值。通过机器学习稳定这些群集，研究人员确保了数据的可转移性，同时降维技术有助于对单个动物进行详细分析。

研究人员提供了一个包含771个自由活动小鼠行为录音的大型数据集，包括应激暴露、药理学和大脑回路干预，旨在揭示隐藏的治疗效应、展示单个动物层面的细微变化并检测特定干预所涉及的大脑过程。

该工作流程兼容主流聚类方法，显著增强了统计效能，并能够预测动物的未来行为。

据悉，啮齿类行为的准确检测和量化是基础生物医学研究的基石。目前的数据驱动方法将自由探索行为分段为不同的群集，但由于多重检验问题，这些方法的统计效能较低，且在实验间的转移性较差，未能充分利用单个动物的丰富行为特征。

附：英文原文

Title: Analysis of behavioral flow resolves latent phenotypes

Author: von Ziegler, Lukas M., Roessler, Fabienne K., Sturman, Oliver, Waag, Rebecca, Privitera, Mattia, Duss, Sian N., OConnor, Eoin C., Bohacek, Johannes

Issue&Volume: 2024-11-12

Abstract: The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple testing, exhibit poor transferability across experiments and fail to exploit the rich behavioral profiles of individual animals. Here we introduce a pipeline to capture each animal’s behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a large dataset of 771 behavior recordings of freely moving mice—including stress exposures, pharmacological and brain circuit interventions—to identify hidden treatment effects, reveal subtle variations on the level of individual animals and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, substantially enhances statistical power and enables predictions of an animal’s future behavior.

DOI: 10.1038/s41592-024-02500-6

Source: https://www.nature.com/articles/s41592-024-02500-6