Large-scale plastic changes of the brain network in an animal model of neuropathic pain

Abstract

Pain is a multidimensional experience emerging from the flow of information in the brain. It is reasonable therefore to understand pathological pain in terms of plasticity of the distributed brain network. Recently, we demonstrated that multivariate pattern analysis of fluorodeoxyglucose micro-positron emission tomography (FDG micro-PET) imaging can successfully identify neuropathic pain animals at the individual level by capturing the distributed patterns of the resting-state brain activity (Kim et al., 2014). Here, we aimed to reveal the underlying plastic changes of the distributed brain network that enabled successful discrimination of neuropathic pain. We analyzed FDG micro-PET images in awake rats with spinal nerve ligation (SNL) (SNL group, n=13; sham group, n=10) that were acquired in our previous study. In order to investigate the altered functional connectivity pattern of the brain network, first, we developed a node set search algorithm that defines the optimal node set representing the whole brain in given brain images and constructed resting-state brain networks with defined nodes. Graph theoretical analysis revealed that SNL resulted in decreased small-worldness and more fragmented modular structure compared to sham group. Connectivity pattern analyses showed that regions in the brainstem, sensorimotor cortex, and some part of the prefrontal cortex became highly connected following SNL, whereas the cerebellum and some prefrontal regions showed decreased connections. In addition, we found close relationships between characteristics of connectivity and metabolic changes. Our findings suggest that neuropathic pain is associated with connectional plasticity of the resting-state brain.