Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain - Li, Zhonghua; Zhao, Leilei; Ji, Jing; Ma, Ben; Zhao, Zhiyong; Wu, Miao; Zheng, Weihao; Zhang, Zhe (Frontiers in Neurology, 2022)
Chronic back pain (CBP) is a maladaptive health problem affecting 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 CBP patients and 34 age-matched healthy controls (HC) in OpenPain database via 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 CBP patient to 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 five-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 CBP cohort. These results highlighted spatiotemporal changes of 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.