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April 5, 2023

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Caleb Weinreb, Mohammed Abdal Monium Osman, Libby Zhang, Sherry Lin, Jonah Pearl, Sidharth Annapragada, Eli Conlin, Winthrop F. Gillis, Maya Jay, Ye Shaokai, Alexander Mathis, Mackenzie Weygandt Mathis, Talmo Pereira, Scott W. Linderman, and Sandeep Robert Datta (2023)

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules (“syllables”) from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

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HISTORY SHOWS AGAIN AND AGAIN HOW NATURE POINTS OUT THE FOLLY OF MEN – “GODZILLA,” BLUE OYSTER CULT

Sandeep Robert Datta, MD, Ph.D Department of Neurobiology Harvard Medical School