How does a skeleton move? A novel tracking method to quantify skeletal kinematics in freely moving rodents

How can we measure the movement of a skeleton in a furry animal as it moves through its environment? Researchers at the Max Planck Institute for Behavioral Neurobiology have developed a method to quantify skeletal movement in freely moving rodents to a new level of accuracy and detail.

It is based on the construction of a skeleton model that calculates the motion of the bone joint using basic anatomical principles, such as the limits of joint rotation and the speeds at which the bodies can move. This approach, published in Nature Methodsopens up a new ability to read how animals interact with their environment and begins to unravel the relationship between neural activity and complex behavior such as decision-making.

Have you ever thought about how your skeleton moves as you go about your day? When we think about this question, images of X-rays immediately come to mind. But how would we measure the movement of a skeleton without using X-rays on an animal running or jumping around and interacting with its environment? And why would that matter?

Studying the free-moving animal provides unparalleled insights into how animals behave and make decisions, for example when avoiding predation, finding mates and raising their young. While many studies have measured animal behavior, studies measuring the mechanics of how they move are lacking. But since activity in the central nervous system ultimately leads to decisions made through body movements, measuring these mechanisms and relating them to neural activity is essential to gaining deep insights into brain function.

Without an X-ray machine, analyzing the movements of individual bones is extremely difficult, as the occlusion of fur, skin and soft tissue complicates the measurement of skeletal movement. Recently, many advanced machine learning methods have been able to accurately measure an animal’s posture and even changes in an animal’s facial expression. However, so far none of the existing techniques have been able to track changes in bone positions and joint movement below the visible surface of the body.

Researchers from the Behavior and Brain Organization department at the Max Planck Institute for Neurobiology of Behavior in Bonn (MPINB), led by Prof.Dr. Jason Kerr, have now developed a video-based method for 3D skeletal tracking to analyze individual joints in untethered animals while they interact with their environment.

Their anatomically constrained model (ACM) is based on an anatomically grounded skeleton that infers the skeletal kinematics of an animal as it moves freely. From this data it was possible to measure the inner workings of a skeleton, moment by moment, as the animals jumped, walked, stretched and ran around.

This new approach can be applied to multiple furry species such as mice and rats of different sizes and ages. To ensure the data was correct, the researchers worked with colleagues from the Max Planck Institute for Biological Cybernetics and the Max Planck Institute for Intelligent Systems in Tuebingen using MRI scans of the animals to compare the ACM model with the actual skeleton.

“Our new method is relatively simple, tether-free and uses overhead cameras. It solves many problems associated with tracking freely moving rodents, especially those covered in fur and when the body covers the legs and feet,” he says Professor Dr. Jason Kerr, who led the study with Professor Dr. Jakob Macke from Tuebingen.

One of the next steps is to combine this approach with simultaneous recordings from neurons in the brain using the tiny head-mounted multiphoton microscopes developed by researchers at MPINB. This would allow precise correlation of neural activity with actual behavior to learn more about how the brain controls even complex behavior.

The researchers will also apply their new method to measure kinematic movement in other animal species in more natural settings and simultaneously in multiple interacting animals. “Using our new method, on the one hand we will gain further insights into how animals interact with their environment and, on the other hand, we hope to gain insight into how animals interact with each other,” says Jason Kerr.