Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments

Abstract

Marker-based motion capture (MBMC) is a powerful tool for precise, high-speed, three-dimensional tracking of animal movements, enabling detailed study of behaviors ranging from subtle limb trajectories to broad spatial exploration. Despite its proven utility in larger animals, MBMC has remained underutilized in mice due to the difficulty of robust marker attachment during unrestricted behavior. In response to this challenge, markerless tracking methods, facilitated by machine learning, have become the standard in small animal studies due to their simpler experimental setup. However, trajectories obtained with mar kerless approaches at best approximate ground-truth kinematics, with accuracy strongly dependent on video resolution, training dataset quality, and computational resources for data processing. Here, we overcome the primary limitation of MBMC in mice by implanting minimally invasive markers that remain securely attached over weeks of recordings. This technique produces high-resolution, artifact-free trajec tories, eliminating the need for extensive post-processing. We demonstrate the advantages of MBMC by resolving subtle drug-induced kinematic changes that become apparent only within specific behavioral contexts, necessitating precise three-dimensional tracking beyond simple flat-surface locomotion. Furthermore, MBMCuniquely captures the detailed spatiotemporal dynamics of harmaline-induced trem ors, revealing previously inaccessible correlations between body parts and thus significantly improving the translational value of preclinical tremor models. While markerless tracking remains optimal for manybehavioral neuroscience studies in which general posture estimation suffices, MBMC removes bar riers to investigations demanding greater precision, reliability, and low-noise trajectories. This capability significantly broadens the scope for inquiry into the neuroscience of movement and related fields.