Journal Article

DZNE-2022-01721

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Identifying behavioral structure from deep variational embeddings of animal motion.

Luxem, K. (First author)DZNE* ; Mocellin, P.DZNE* ; Fuhrmann, F.DZNE* ; Kürsch, J.DZNE* ; Miller, S. R. ; Palop, J. J. ; Remy, S.DZNE* ; Bauer, P. (Last author)DZNE*

2022
Springer Nature London

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Please use a persistent id in citations: doi:10.1038/s42003-022-04080-7

Abstract: 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 high-dimensional 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 robust approach for the segmentation of animal motion that is applicable to a wide range of experimental setups, models and conditions without requiring supervised or a-priori human interference.

Keyword(s): Animals (MeSH) ; Humans (MeSH) ; Behavior, Animal (MeSH) ; Motion (MeSH) ; Neurosciences (MeSH)

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Contributing Institute(s):

1. Neuronal Networks (AG Remy)

Research Program(s):

1. 351 - Brain Function (POF4-351) (POF4-351)

Appears in the scientific report 2022

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