RT Journal Article
SR Electronic
T1 Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.14.095430
DO 10.1101/2020.05.14.095430
A1 Luxem, Kevin
A1 Mocellin, Petra
A1 Fuhrmann, Falko
A1 Kürsch, Johannes
A1 Remy, Stefan
A1 Bauer, Pavol
YR 2022
UL http://biorxiv.org/content/early/2022/01/14/2020.05.14.095430.abstract
AB Quantification and detection of the hierarchical organization of behavior is a major challenge in neuroscience. Recent advances in markerless pose estimation enable the visualization of highdimensional spatiotemporal behavioral dynamics of animal motion. However, robust and reliable technical approaches are needed to uncover underlying structure in these data and to segment behavior into discrete hierarchically organized motifs. Here, we present an unsupervised probabilistic deep learning framework that identifies behavioral structure from deep variational embeddings of animal motion (VAME). By using a mouse model of beta amyloidosis as a use case, we show that VAME not only identifies discrete behavioral motifs, but also captures a hierarchical representation of the motif’s usage. The approach allows for the grouping of motifs into communities and the detection of differences in community-specific motif usage of individual mouse cohorts that were undetectable by human visual observation. Thus, we present a novel and robust approach for quantification of animal motion that is applicable to a wide range of experimental setups, models and conditions without requiring supervised or a-priori human interference.Competing Interest StatementThe authors have declared no competing interest.