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Estimation of skeletal kinematics in freely-moving rodents

Citation

Monsees, Arne et al. (2022), Estimation of skeletal kinematics in freely-moving rodents, Dryad, Dataset, https://doi.org/10.5061/dryad.g4f4qrfsw

Abstract

Forming a complete picture of the relationship between neural activity and skeletal kinematics requires quantification of skeletal joint biomechanics during free behavior. However, without detailed knowledge of the underlying skeletal motion, inferring limb kinematics during free motion using surface tracking approaches is difficult, especially for animals where the relationship between the surface and underlying skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinematic quantification of an anatomically defined skeleton in untethered freely-behaving rats and mice. This skeleton-based model was constrained using anatomical principles and joint motion limits and provided skeletal pose estimates for two rodent species and a range of body sizes, even when limbs were occluded. Model-inferred limb positions and joint kinematics during gait and gap-crossing behaviors were verified by direct measurement of either limb placement or limb kinematics using inertial measurement units (IMU). Together we show that complex decision-making behaviors can be accurately reconstructed at the level of skeletal kinematics using our anatomically constrained model.

Usage Notes

Data is processed by the Python 3 scripts published on Github and also included here. Data is provided in the structure required by the scripts. A base path may have to be adjusted in ACM/data.py or the head of the respective script. Linked scripts generate the figures published in related manuscript.

A description of the data is included in the respective folders.

Funding

Max-Planck-Gesellschaft

International Max Planck Research School for Brain and Behavior

Deutsche Forschungsgemeinschaft, Award: DFG SCHE 658/12

Deutsche Forschungsgemeinschaft, Award: EXC-Number 2064/1, Project number 390727645