Motor neural dynamics of free behavior enabled through 3D computer vision

Motor systems neuroscience seeks to understand the neural mechanisms behind voluntary movement. The last two decades have witnessed a transformation in this ?eld with the use of multielectrode recordings and statistical estimation and modeling techniques. These technological advances have yielded rich, low-dimensional neural dynamics that are suggestive of the mechanisms underlying behavior. To minimize confounds, the overwhelming majority of these studies utilize behavioral constraint to isolate just the behaviors of interest for study. While effective for generating many behav- iorally similar trials, this may have the unintentional consequence of arti?cially constraining neural dynamics to a subset of its full range. This project seeks to better understand whether and how neural dynamics change with respect to the behavioral context (constrained vs unconstrained) they occur in. This type of work has historically been challenging because capturing limb kinematics in an unconstrained setting is non-trivial. However, with recent advances in computer vision technology, accurate 3D cameras have become accessible tools for research. This study will leverage these new 3D cameras to capture unconstrained behavior in a large observational enclosure. Novel algorithms for the processing of these 3D datasets will be used to estimate the subject's pose. These limb kinematics will be synchronized and correlated against neural data recorded from one or more 96-channel Utah electrode array(s) implanted in motor regions of cortex. Low-dimensional neural dynamics can be generated from this synchronized data. The dynamics will be explored in the context of two behaviors in the enclosure: walking and reaching for food on the ?oor. The dimensionality of the dynamics in these two contexts will be compared, with the null hypothesis stating that there is no difference in dimensionality of dynamics between these behavioral contexts. A subsequent experiment will be to again construct low-dimensional neural dynamics, but this time include a context of behaviorally constrained reaching. The dynamics from these three contexts will be compared to ?nd a common subspace (subset of dimensions) shared among all. This subspace, if it exists (the null hypothesis is that there is no difference in the dynamics between the behavioral contexts), represents fundamental dynamics that are invariant of the behavioral context, suggestive of causal necessity of this subspace. Taken together, these studies will further our understanding of how low-dimensional neural dynamics drive motor be- havior. This insight has implications for the development of ambulatory brain-machine interfaces and may inform the treatment of individuals with motor disorders such as stroke.