Department of Physiology, Seoul National University College of Medicine, Seoul 110-799, Republic of Korea.
Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul 110-799, Republic of Korea; Seoul National University Boramae Medical Center, Seoul 156-707, Republic of Korea.
Neuroimage. 2014 Sep;98:203-15. doi: 10.1016/j.neuroimage.2014.04.063. Epub 2014 May 2.
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.
疼痛是一种源自大脑信息流的多维体验。因此，从分布式大脑网络的可塑性来理解病理性疼痛是合理的。最近，我们通过对静息态脑活动的分布模式进行多元模式分析，成功地证明了氟脱氧葡萄糖微正电子发射断层扫描（FDG micro-PET）成像可以在个体水平上识别出神经性疼痛动物（Kim 等人，2014 年）。在这里，我们旨在揭示使神经性疼痛成功区分的分布式大脑网络的潜在可塑性变化。我们分析了在我们之前的研究中获得的脊髓神经结扎（SNL）清醒大鼠的 FDG micro-PET 图像（SNL 组，n=13；假手术组，n=10）。为了研究大脑网络功能连接模式的改变，首先，我们开发了一个节点集搜索算法，该算法定义了代表给定脑图像中整个大脑的最佳节点集，并构建了具有定义节点的静息态脑网络。图论分析表明，与假手术组相比，SNL 导致小世界特性降低和模块化结构更碎片化。连接模式分析表明，脑干、感觉运动皮层和前额叶皮层的某些部分在 SNL 后变得高度连接，而小脑和前额叶的某些部分显示出连接减少。此外，我们发现连接特征与代谢变化之间存在密切关系。我们的发现表明，神经性疼痛与静息态大脑的连接可塑性有关。
