Examining structural and functional connectivity change in animal models of cerebrovascular disease - Hall, Gerard (2021)
Cerebrovascular disease is a leading cause of death worldwide and is associated with a wide range of cognitive impairments. Using graph theory on structural and functional connectomes generated through MRI is emerging as a promising tool in revealing network change in patients with VCI. Though animal models can offer mechanistic insight into disease progression, there is a lack of tools for processing preclinical MRI data when compared to the clinics, especially for advanced analysis investigating network changes. Therefore, the primary aim of this thesis was to develop an MRI processing pipeline from gold standard human approaches that is adapted for non-human preclinical models. During development, the pipeline was used to process and analyse data from a bilateral carotid artery stenosis (BCAS) mouse model widely used to emulate vascular cognitive impairment (VCI). The subsequent second aim therefore was to identify potential neuroimaging biomarkers of VCI. The most notable changes in the BCAS brains were reductions in functional connectivity in the sensorimotor network, and an increase in structural connectivity in the visual association area at the chronic timepoints. The pipeline was further validated in a contrasting sheep model of cerebrovascular disease, known as the middle cerebral artery occlusion (MCAO) model of stroke. Post-stroke timepoints displayed more specific and focused damage in the white matter on the injured side, this was coupled with a widespread increase in functional connectivity. Our approach represents a significant contribution to preclinical connectivity literature, and future work could help refine and update the tools chosen in the pipeline.