Characterizing the structure of mouse behavior using Motion Sequencing

TL;DR

This paper introduces Motion Sequencing (MoSeq), a protocol using 3D machine vision and unsupervised learning to decompose spontaneous mouse behavior into elemental modules called syllables, facilitating behavioral analysis.

Contribution

The paper presents a standardized protocol and pipeline for MoSeq, making unsupervised behavioral analysis accessible to researchers without extensive computational ethology expertise.

Findings

Enables decomposition of mouse behavior into syllables

Provides visualization tools for behavioral transitions

Facilitates analysis of behavioral changes after manipulations

Abstract

Spontaneous mouse behavior is composed from repeatedly-used modules of movement (e.g., rearing, running, grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the impact of drugs, genes, context, sensory stimuli and neural activity on behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically-inspired method that uses 3D machine vision and unsupervised machine learning to decompose spontaneous mouse behavior in the laboratory into a series of elemental modules called "syllables". This protocol is based upon a notebook-based pipeline for MoSeq that includes modules for depth video acquisition, data pre-processing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other over time. This protocol and the accompanying pipeline significantly lower the bar for adopting this unsupervised, data-driven approach to characterizing mouse behavior, enabling users without significant computational ethology experience to gain insight into how the structure of behavior is altered after experimental manipulations.