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

doi:10.3724/SP.J.1042.2026.0583

摘要/Abstract

摘要：

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。

关键词: 疼痛, 脑网络, 疼痛韧性, 认知功能, 功能磁共振成像

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 buffering the adverse effects 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 insufficiently understood. Previous studies has demonstrated a positive association between cognitive function and resilience, with interventions targeting cognitive interventions shown to enhance 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, the present research program hypothesizes that cognitive function and the CEN are not only pivotal to the enhancement of pain resilience but also predictive of its longitudinal development. To test this hypothesis, a multicenter, longitudinal study integrating self-report measures and functional magnetic resonance imaging (fMRI) is proposed.

This research program comprises three sub-studies. Study 1 adopts a multi-time point longitudinal survey design to examine the relationship between cognitive function and pain resilience among patients with chronic musculoskeletal pain and in healthy controls. Cognitive functioning, pain resilience, and pain-related outcomes are assessed at baseline, 6 months, and 12 months. Cross-lagged panel modeling is used to characterize the directional and temporal associations between cognitive function and pain resilience. Additionally, multi-group structural equation modeling is further applied to determine whether these associations differ between patients and healthy controls.

Study 2 employs a multimodal neuroimaging design across two clinical centers. At baseline (Time Point 1), demographic characteristics, cognitive function, pain outcomes (including pain intensity, pain interference, and depressive symptoms), pain resilience and structural and resting-state fMRI data are collected from patients with chronic musculoskeletal pain. Pain resilience is reassessed at 6 months (Time Point 2) and 12 months (Time Point 3). Mediation analyses are conducted to test whether resting-state functional connectivity within the CEN mediates the association between baseline cognitive function and pain resilience at Time Point 3. Internal CEN connectivity (i.e. functional interactions among nodes within the network) serves as the mediator, with seed regions selected based on prior literature. The mediation model is established using data from Center 1 and independently validated in Center 2, enabling robust identification of neural mechanisms underlying cognitive contributions to pain resilience.

Study 3 extends the first two studies by developing a multimodal deep learning prediction model using a multicenter prospective framework to predict pain resilience. Guided by the multimodal integration framework, the model incorporates complementary feature domains, including demographic variables, pain outcome indicators, cognitive function measures, and neuroimaging features. Demographic variables enhance clinical applicability and mitigate group-level confounds (e.g., age-related brain changes), thereby improving interpretability of neural predictors. Pain outcomes index clinical symptomatology, cognitive function measures capture psychological context, and neuroimaging features provide a biological foundation. By integrating these multimodal data, the model aims to identify robust, clinically meaningful biomarkers of pain resilience. At baseline (Time Point 1), all domains are assessed. Pain resilience, cognitive function and pain outcomes are reassessed at 6 months (Time Point 2) and 12 months (Time Point 3), with pain resilience additionally evaluated at 18 months (Time Point 4). A Gated Recurrent Unit (GRU) model is used to integrate longitudinal (cognitive function and pain outcomes) and static baseline data (demographics and neuroimaging features) to predict pain resilience at Time Point 4. Training is conducted using data from Center 1, with external validation in Center 2 to ensure robust individualized prediction.

Collectively, this research program is expected to clarify the cognitive and neural mechanisms through which cognitive function shapes psychological resilience in individuals with chronic pain. By delineating the cognitive processes and CEN-based neural pathways that support pain resilience, the findings are positioned to advance theoretical models of adaptive coping in chronic pain and inform the development of targeted, personalized interventions for chronic pain management.

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

中图分类号:

B845

游贝贝, 顾怀飞, 文宏伟. (2026). 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. 心理科学进展 , 34(4), 583-596.

YOU Beibei, GU Huaifei, WEN Hongwei. (2026). The role and predictive mechanisms of cognitive function and the central executive network in pain resilience. Advances in Psychological Science, 34(4), 583-596.

图/表 2

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