International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China.
Jinan Engineering Laboratory of Human-Machine Intelligent Cooperation, Jinan 250353, P. R. China.
Int J Neural Syst. 2023 May;33(6):2350030. doi: 10.1142/S0129065723500302. Epub 2023 May 15.
Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.
中枢神经性疼痛（CNP）是脊髓损伤（SCI）后的一种并发症，与大脑皮层的可塑性有关。脑电图（EEG）信号记录的皮层可塑性可以作为 CNP 的生物标志物。为了分析损伤和疼痛共同作用下或疼痛作用下的大脑网络机制变化，本文主要研究 SCI 后伴有神经性疼痛和不伴有神经性疼痛患者的大脑网络功能连接变化。本文记录了 SCI 后伴有 CNP 组、SCI 后不伴有 CNP 组和健康对照组的 EEG。已使用锁相值构建大脑网络拓扑连接图。通过比较两组 SCI 患者与健康组的大脑网络，发现[Formula: see text]和[Formula: see text]频段中，损伤增加了患者额叶与枕叶、颞叶和顶叶之间的功能连接。此外，对 SCI 后伴有 CNP 组和不伴有 CNP 组的大脑网络进行比较，发现疼痛对患者额叶内连接的增加有更大的影响。已使用 CNP 患者的运动想象（MI）数据提取一维局部二值模式（1D-LBP）和公共空间模式（CSP）特征，对患者的左右手运动进行分类。所提出的 LBP-CSP 特征方法达到了 98.6%的最高准确率和 91.5%的平均准确率。本研究的结果对 CNP 患者的神经康复和脑机接口具有重要的临床意义。
