Extended Data Fig. 2: MoSeq captures subsecond structure in spontaneous mouse behaviour.

a) Distribution of syllable durations identified by MoSeq. The mean/median syllable duration was 566/400 ms +/− 636 ms SD. b) The average number of times each MoSeq-identified syllable is used during a 30-minute experiment per mouse (n = 16 mice). Error bars indicate bootstrap 95% confidence intervals across mice. c) Human-annotated descriptions of observed behavioural syllables. Left: semantic labels and “spinograms” of all behavioural syllables used more than 1% of the time, here to provide an illustration of movements associated with syllables. Each trace in the spinogram is an average height profile of the mouse computed by taking the pixel values along the center of the depth image across columns (note that MoSeq pre-processes depth images so that mice always face to the right of the cropped depth image). Each trace from left to right is the average of each frame of the behavioural syllable from the beginning to end. The distances between successive traces are proportional to the average x/y displacement from one frame to the next. Spinograms are color-coded by the average angular velocity of the syllable. Right: dendrogram computed using the pairwise MoSeq distance of all behavioural syllables (see Methods for a description of how MoSeq distances, which capture the average three-dimensional pose dynamics of each syllable, are computed). Spinograms are aligned to their corresponding leaf in the dendrogram. d) The average transition matrix visualized as a state map. Each circle corresponds to a syllable, and each arrow corresponds to the likelihood that there is a transition from one syllable to the next. Arrow width indicates transition probability. All transitions with a probability below 0.1 are removed for visual clarity in this statemap and in all subsequent statemaps. e) Kinematic parameters over time averaged across all experiments and mice, demonstrating non-stationarities in kinematics across each recording experiment. Lines indicate boundaries derived via k-means clustering of this data. Note that these boundaries were used for analysis shown in Fig. 2e and k. Specifically, in order to prevent non-stationarities from impacting within-experiment correlations, correlations were computed within each of these segments and then averaged. f) Heatmap of syllable counts computed over a six-minute sliding window for the 37 syllables used >1% of the time in an example experiment. Syllables are sorted by total usage in the experiment, with the most-used syllable at the top and least used on the bottom. The colors above each segment of the plot indicate the time intervals used to compute the transition matrices in Extended Data Fig. 2g. g) State maps computed for each colored section of the example experiment shown in Extended Data Fig. 2f, summarizing the transition statistics between behavioural syllables, and demonstrating that transitions are also non-stationary over each imaging experiment. Each node is a syllable, and each line represents the transition from one syllable to the next (whose width specifies the observed likelihood of each transition, per the legend).