Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study

Abstract

Abstract Pain empathy enables us to understand and share how others feel in the context of pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, chronic pain patients have an increased risk for abnormal pain empathy, and the association between the whole-brain functional network, chronic pain, and pain empathy remains unclear. Using resting state functional magnetic resonance imaging (fMRI) and machine learning analysis, we investigated the static and dynamic functional network connectivity (FNC) in predicting pain empathy scores in 41 healthy controls (HCs) and 45 women with primary dysmenorrhea (PDM). In addition, a classification analysis was performed to study the FNC differences between HCs and PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state menstrual pain were recorded. In study 1, results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. This finding was validated in an independent dataset. In study 2, PDM exhibited a distinctive prediction network for pain empathy. The predictive features were concentrated in the sensorimotor network (SMN) and exhibited a significant association with trait menstrual pain. Moreover, the SMN-related dynamic FNC could accurately distinguish PDM from HCs. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that chronic pain may affect pain empathy through the maladaptive dynamic interaction between brain networks.