Characterizing the Dynamics of Movement in \textit{Drosophila Melanogaster} and Humans With Autistic Spectrum Disorder

Abstract

In this thesis, I seek to understand the dynamics and geometry behind two forms of movement by formulating a generative model that encapsulates key features of the movement. The first form of movement involves optogenetically stimulated Drosophila melanogaster walking in a two-dimensional virtual reality environment with a grid of obstacles. This virtual paradigm allows us to analyze the dynamics of the flies' movement and identify two major modes of movement: exploring and traveling. We demonstrate that these modes of movement serve as the underpinnings of trends involving speed, angular velocity, and curvature that we observe in the data. The second form of movement involves the tracing capabilities of people with Autistic Spectrum Disorder (ASD), for whom research has revealed significant differences in motor movement as compared to the neurotypical population, but the mechanisms driving these differences are largely unknown. We examine the tracing movements of both groups and identify tracking delay as a possible mechanism for this disparity in the tracing task. For both of these forms of movement, we develop a generative model informed by our analysis of the data and demonstrate its validity by replicating identified trends in a simulated data set. This suggests that the key features we identify in both scenarios play a role in governing the dynamics of the two forms of movement.