认知功能和中央执行网络对疼痛心理韧性的作用和预测机制OACHSSCD
The role and predictive mechanisms of cognitive function and the central executive network in pain resilience
慢性疼痛严重影响患者的身心健康和社会功能,亟需有效的应对与管理策略.心理韧性对缓解疼痛的负面影响至关重要,提升疼痛心理韧性成为患者应对身心挑战的关键,然而哪些因素对疼痛心理韧性的提升具有关键作用及其机制尚未明确.既往研究表明,心理韧性与认知功能呈正相关,且认知功能干预可提升心理韧性.在此基础上,有研究进一步采用神经影像与机器学习技术发现,中央执行网络皮层区的灰质体积和功能活动水平与疼痛心理韧性相关.因此本研究假设"认知功能与中央执行网络不仅对提升疼痛心理韧性具有关键作用,还能预测其发展",并拟采用多中心多时间点的自我报告和脑功能MRI数据,①运用交叉滞后分析揭示认知功能与疼痛心理韧性的关联模式;②探索中央执行网络的静息态功能连接在认知功能与疼痛心理韧性关系中的中介角色;③运用门控循环单元这一时序数据建模的深度学习算法构建并验证疼痛心理韧性的多模态预测模型.本研究为探索慢性疼痛应对的神经影像学基础开辟了新的视角,为开发更有效的疼痛管理和精准治疗策略提供科学依据.
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 face 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.Building on this work,studies employing neuroimaging and machine learning techniques 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.
游贝贝;顾怀飞;文宏伟
贵州医科大学护理学院,贵安新区 561113贵州医科大学护理学院,贵安新区 561113川北医学院核医药与辐射安全防控四川省重点实验室||川北医学院医学影像学院,四川南充 637100
社会科学
疼痛脑网络疼痛韧性认知功能功能磁共振成像
painbrain networkspain resiliencecognitive functionfunctional magnetic resonance imaging
《心理科学进展》 2026 (4)
583-596,14
国家自然科学基金项目(32460209,32100902)贵州省研究生教育创新计划项目(2024YJSJGXM062)贵州省省级科技计划项目(黔科合基础-[2024]青年241)四川省自然科学基金项目(2025NSFSC2149)贵州医科大学国家自然科学基金培育项目(22NSFCP41).
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