Long-term connectome analysis reveals reshaping of visual, spatial networks in a model with vascular dementia features

Background and purpose: Connectome analysis of neuroimaging data is a rapidly expanding field that offers the potential to diagnose, characterise, and predict neurological disease. Animal models provide insight into biological mechanisms that underpin disease, but connectivity approaches are currently lagging in the rodent. Methods: We present a pipeline adapted for structural and functional connectivity analysis of the mouse brain and we tested it in a mouse model of vascular dementia. Results: We observed lacunar infarctions, microbleeds, and progressive white matter change across 6 months. For the first time, we report that default mode network activity is disrupted in the mouse model. We also identified specific functional circuitry that was vulnerable to vascular stress, including perturbations in a sensorimotor, visual resting state network that were accompanied by deficits in visual and spatial memory tasks. Conclusions: These findings advance our understanding of the mouse connectome and provide insight into how it can be altered by vascular insufficiency.