Understanding the Neural Correlates of Recovery in a Non- Human Primate Model of Ischemic Stroke

Abstract

Stroke is a leading cause of long-term disability worldwide, and understanding the neural substrates that underlie stroke recovery is of paramount importance for developing effective interventions. However, stroke recovery in humans exhibits substantial variability across individuals, posing challenges for treatment customization and prediction of outcomes. While small animal models, such as rodents, have enhanced our understanding of stroke pathophysiology and recovery, their translational relevance to humans remains limited due to anatomical, physiological, and behavioral disparities. Non-human primate (NHP) models of stroke represent a bridge this gap due to their immense similarities to humans. These NHP models allow for whole-brain imaging before and after precisely controlled stroke lesions, which is not possible in humans. Thus, the model could provide novel insights into neural substrates underlying recovery unseen in humans or rodents. The following chapters of this doctoral thesis aim to investigate the complex nature of stroke recovery, by utilizing a previously validated non- human primate (NHPs) stroke model and cutting edge structural and functional neuroimaging techniques. The first topic of this thesis examining the brain’s structural correlates of recovery in a middle cerebral artery occlusion model of non-human primates. We show that preservation of sensory and association cortices plays a key role in the recovery process of animals. The second topic of this thesis is concerned with elucidating the functional brain changes that occur pre- to post-stroke. We highlight that considerable functional changes occur throughout the brain, and that sensorimotor and cognitive networks may play a critical role in the degree of recovery that animals exhibit. Lastly, the final topic of this thesis is aimed at determining if pre-stroke brain variation can be used to predict outcomes following an ischemic lesion. We show that we can predict recovery with considerable accuracy (>70%) using only individual variability of the pre-stroke functional scans, suggesting that there are inherent connections throughout the brain that may be exploited during the recovery process following stroke.