Abstract: Chronic pain profoundly undermines patients’ physical, psychological, and social functioning, highlighting the urgent need for effective coping and management strategies. Psychological resilience plays a pivotal role in mitigating the adverse impact of pain, and enhancing resilience has become essential for patients to facing biopsychosocial challenges. However, the key factors that promote pain resilience and their underlying mechanisms remain unclear. Previous studies indicate a positive association between cognitive function and resilience, with interventions targeting cognitive interventions shown to strengthen resilience. Our preliminary neuroimaging and machine learning work further revealed that gray matter volume and functional activity within cortical regions of the central executive network (CEN) are associated with pain resilience. Building on these findings, we hypothesize that cognitive function and the central executive network are not only pivotal for enhancing pain resilience but also predictive of its development. To test this, we will conduct a multicenter, longitudinal study combining self-report assessments and functional MRI data. Specifically, we will: 1) apply cross-lagged panel analysis to uncover temporal associations between cognitive function and pain resilience; 2) examine the mediating role of resting-state functional connectivity within the central executive network in the relationship between cognitive function and pain resilience; and 3) employ a Gated Recurrent Unit (GRU)–based deep learning algorithm for temporal data modeling to construct and validate a multimodal predictive model of pain resilience. This study offers a novel neuroimaging perspective on chronic pain coping and provides a scientific foundation for developing more effective pain management and precision treatment strategies.

Key words: pain, brain networks, pain resilience, cognitive function, functional magnetic resonance imaging