Li Zhonghua, Zhao Leilei, Ji Jing, Ma Ben, Zhao Zhiyong, Wu Miao, Zheng Weihao, Zhang Zhe
Department of Rehabilitation Medicine, Gansu Provincial Hospital of TCM, Lanzhou, China.
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Front Neurol. 2022 Jun 10;13:899254. doi: 10.3389/fneur.2022.899254. eCollection 2022.
Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.
慢性背痛（CBP）是一种影响患者脑功能和行为的适应性不良健康问题。越来越多的证据表明，CBP可能会改变功能性脑网络的组织；然而，CBP的严重程度是否与功能性网络拓扑结构动态变化相关仍不清楚。在此，我们基于OpenPain数据库中34例CBP患者和34例年龄匹配的健康对照（HC）的静息态功能磁共振成像（rs-fMRI），采用滑动窗口方法生成动态功能网络，并提取所有窗口的节点度、聚类系数（CC）和参与系数（PC）作为特征，以表征时间尺度上网络拓扑结构的变化。我们提出了一种名为时间分级指数（TGI）的新特征，用于量化CBP患者每个网络属性相对于HCs正常振荡的时间偏差。通过五折交叉验证策略，这三个特征的TGI在三种常用回归模型（即支持向量回归、套索回归和弹性网络回归）中预测疼痛强度方面表现出色，最小均方误差为0.25±0.05；并且TGI与患者的抑郁症状无关。此外，与HCs相比，对预测贡献最大的脑区在CBP队列的各个时间窗口中显示出显著更高的CC和更低的PC。这些结果突出了CBP患者功能性网络拓扑结构的时空变化，这可能作为评估脑内疼痛感觉的有价值生物标志物，并可能促进CBP管理/治疗方法的发展。
