Nature CommunicationsNeurosciencePeer-reviewed journal article
Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress
Joeri BordesiD, Lucas MirandaiD, Maya ReinhardtiD, Sowmya V. NarayaniD, Jakob HartmanniD, Emily L. NewmaniD, Lea M. BrixiD, Lotte van DoeselaariD, Clara EngelhardtiD, Larissa J. DillmanniD, Shiladitya MitraiD, Kerry James RessleriD, Benno PütziD, Felix V. AgakoviD, Bertram Müller‐MyhsokiD, Mathias V. SchmidtiD
Abstract
Severe stress exposure is a global problem with long-lasting negative behavioral and physiological consequences, increasing the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and social behavioral changes. The current developments of markerless pose estimation tools allow for more complex and socially relevant behavioral tests, but the application of these tools to social behavior remains to be explored. Here, we introduce the open-source tool "DeepOF" to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut annotated pose estimation data. The supervised pipeline relies on pre-trained classifiers to detect defined traits for both single and dyadic animal behavior. Subsequently, the unsupervised pipeline explores the behavioral repertoire of the animals without label priming, which has the potential of pointing towards previously unrecognized motion motifs that are systematically different across conditions. We here provide evidence that the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter. The stress-induced social behavior shows a state of arousal that fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. DeepOF aims to facilitate reproducibility and unification of behavioral classification of social behavior by providing an open-source tool, which can significantly advance the study of rodent individual and social behavior, thereby enabling novel biological insights as well as drug development for psychiatric disorders.
- organism: mouse
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
- Citations
- 81
- FWCI
- 7.34
- Field percentile
- top 2%
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