Of Movement and Mice: A Study of Movement Variability Using Marker-based 3D Motion Capture and Mathematical Representations of Locomotion on a Treadmill

Abstract

Okinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyLocomotion is an important output of the central nervous system (CNS) and an essential component of many behaviours. The inherent variability of animal locomotory movements is the key to understanding how living things move so well. Most studies asses mouse locomotion using central metrics such as step-length, speed, angular excursions, and phase differences of limbs. The limitation of such approaches is that by describing variability as just the degree of spread around some measure of centrality we lose the ability to look at the dynamics. Additionally, there is mounting evidence that variability encodes dynamic signatures that are key to understanding both the function and dysfunction of the CNS. Few studies examine the whole body of the animal as it moves, particularly in tasks that involve more natural movements. Fewer still, that do this in all three spatial dimensions (3D). However, locomotion is a whole bodied movement, and most organisms move in 3D. Therefore, there is a need to study whole bodied movements in 3D, with sufficient spatio-temporal resolution to analyse their inherent variability. This entails using observational methods capable of capturing natural movements alongside analytical methods to interpret the data. In this thesis, I present my work on obtaining mathematical representations of voluntary treadmill locomotion of mice. I use a novel marker assisted 3D motion capture system, adapted for mice, to obtain a 30 dimensional trajectory of the whole body as they run unrestrained on a treadmill set at different speeds. I use principal component analysis to get a basis set of vectors that represent the changes in the body configuration of the mice during treadmill locomotion. Additionally, I use delay embedding techniques to untangle non-stationarities in the data to uncover the different classes of body movement cycles. This approach enables the characterisation of the whole body of the mouse as it locomotes on a treadmill and sets the stage for systematically studying the effect of pharmacological perturbations and different neurological conditions on movement.doctoral thesi