抑郁症与焦虑障碍共病的双重认知控制模式：基于机器学习的症状严重程度预测*

焦江丽; 王田

doi:10.16719/j.cnki.1671-6981.20260506

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心理科学 ›› 2026, Vol. 49 ›› Issue (5) : 1087-1097. DOI: 10.16719/j.cnki.1671-6981.20260506

基础、实验与工效

抑郁症与焦虑障碍共病的双重认知控制模式：基于机器学习的症状严重程度预测^*

焦江丽 ¹^,²^,^** ,
王田 ²

作者信息 +

Dual Mechanisms of Control in Comorbid Major Depression and Anxiety Disorders: Machine Learning-Based Prediction of Symptom Severity

Jiao Jiangli ¹^,²^,^** ,
Wang Tian ²

Author information +

文章历史 +

摘要

传统抑郁症与焦虑障碍共病的诊断方法主要将独立疾病简单叠加，忽视了症状间的动态交互与异质性。研究采用AX-连续性操作测验结合机器学习方法，以抑郁症与焦虑障碍共病组50人、抑郁症组50人、焦虑障碍组51人和健康组50人为被试，探讨共病者的双重认知控制特征，并构建症状严重程度的预测模型。结果发现： (1) 共病组在AY试次的反应时显著长于其他组，在BX试次的正确率显著低于健康组；其反应时的主动性控制指数大于零且显著低于健康组；其正确率的主动性控制指数小于零； (2) 随机森林回归模型特征值重要性分析发现，AY与BX试次行为表现是对综合诊断预测贡献最大的特征，预测模型表现优于单一诊断模型。结果说明，抑郁症与焦虑障碍共病表现出主动性控制减弱—反应性控制代偿性增强的认知控制模式；其指标可为精神健康评估工具开发提供部分客观行为标记。

Abstract

Major depressive disorder (MDD) and anxiety disorders (AD) exhibit high rates of comorbidity, presenting a significant challenge for clinical diagnosis and treatment. Traditional diagnostic frameworks often conceptualize these conditions as distinct entities, potentially overlooking the complex, interactive nature of their symptomatology and the shared cognitive mechanisms that may underlie both. Cognitive control, the ability to regulate thoughts and actions in accordance with internal goals, is a core deficit transdiagnostically associated with both MDD and AD. The contemporary dual-mechanisms of control theory posits two distinct modes: proactive control and reactive control. Understanding the profile of these control modes in comorbid MDD and AD (MDD-AD) could provide crucial insights into the disorder's mechanisms and inform more precise assessment tools. This study aimed to (1) investigate the specific characteristics of proactive and reactive cognitive control in individuals with MDD-AD compared to those with single diagnoses and healthy controls, and (2) construct and validate a machine learning-based model capable of predicting overall symptom severity using behavioral indices from a cognitive control paradigm.

The study design involved four groups of participants: 50 individuals in the MDD-AD group, 50 in the MDD-only group (MDD-o), 51 in the AD-only group (AD-o), and 50 in the healthy control group (HC). Participants were screened using the Zung Self-Rating Depression Scale (SDS) and Zung Self-Rating Anxiety Scale (SAS). Through the AX-CPT task, the reaction times, accuracy, and the relative strength of proactive and reactive control modes (Proactive Behavioral Index, PBI) were assessed. Additionally, a Random Forest (RF) regression model was used to predict symptom severity based on reaction times and accuracy, evaluating the applicability of the model across different groups.

The results showed that the MDD-AD group had significantly longer reaction times compared to other groups in the AX-CPT task, and significantly lower accuracy on BX trials compared to the HC group. Specifically, the reaction times in AX trials were 646 ms for the MDD-AD group, compared to 401 ms for the HC group (p <.001). In BX trials, MDD-AD participants exhibited significantly lower accuracy (.85) compared to the HC group (.99, p <.001). The Proactive Behavioral Index (PBI) indicated that MDD-AD participants showed significantly lower proactive control (PBI_RT =.10) compared to HC participants (PBI_RT =.20, p <.001). Moreover, MDD-AD participants exhibited compensatory enhancement of reactive control in the accuracy-based PBI, with PBI_ACC values of -.18, which was significantly lower than that of the AD-o group (-.40, p <.05), and higher than the MDD-o group (.22, p <.001). These findings suggest that in MDD-AD, proactive control is impaired while reactive control is enhanced.

Machine learning analysis demonstrated that the RF regression model, particularly the comprehensive diagnostic model, outperformed single-diagnosis models in predicting symptom severity. The comprehensive diagnostic model achieved a mean absolute error (MAE) of 5.23, with an R² of.78, indicating strong predictive accuracy. SHAP (SHapley Additive exPlanations) analysis revealed that reaction times in AY and BX trials were key features in predicting symptom severity. The model's feature importance ranking was stable, with the most significant features being the behavioral performance in these trials ( ρ =.89).

In conclusion, individuals with MDD-AD exhibit a distinct cognitive control mode characterized by reduced proactive control and compensatory enhancement of reactive control. The machine learning-based prediction model, utilizing the AX-CPT task, effectively predicts symptom severity and provides theoretical support for the precise assessment and intervention of comorbid disorders. Future research may further integrate multimodal data and expand sample sizes to validate the generalizability of the dual cognitive control model in other psychiatric conditions and explore its underlying neural mechanisms.

导出引用

焦江丽, 王田. 抑郁症与焦虑障碍共病的双重认知控制模式：基于机器学习的症状严重程度预测^*[J]. 心理科学. 2026, 49(5): 1087-1097 https://doi.org/10.16719/j.cnki.1671-6981.20260506

Jiao Jiangli, Wang Tian. Dual Mechanisms of Control in Comorbid Major Depression and Anxiety Disorders: Machine Learning-Based Prediction of Symptom Severity[J]. Journal of Psychological Science. 2026, 49(5): 1087-1097 https://doi.org/10.16719/j.cnki.1671-6981.20260506

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