Large-scale plastic changes of brain networks in the spinal nerve ligation 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. Understanding altered connectivity patterns of the whole-brain network in pathological pain would give an opportunity to diagnose subjective pain disorders in an objective manner. Here, we acquired [18F]fluorodeoxyglucose micro-positron emission tomography (FDG microPET) images in awake rats with spinal nerve ligation (SNL) (SNL group, n = 13

sham group, n = 10). In order to investigate the altered functional connectivity pattern of the brain network, 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 the defined nodes. Graph theoretical analyses revealed that SNL resulted in decreased small-worldness and more fragmented modular structure compared to sham group. Connectivity pattern analyses showed that the regions in the brainstem, sensorimotor cortex, and some 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. Based on our findings, we developed a connectivity pattern-based diagnostic tool for neuropathic pain, and could classify neuropathic brain with high accuracy (92.31 % sensitivity, 90.00 % specificity, and 91.30 % total accuracy). These results suggest that neuropathic pain is associated with connectional plasticity of the resting-state brain and this enables to develop an objective diagnostic tool.