ISSN 1671-3710
CN 11-4766/R
主办：中国科学院心理研究所
出版：科学出版社

心理科学进展 ›› 2025, Vol. 33 ›› Issue (6): 887-904.doi: 10.3724/SP.J.1042.2025.0887 cstr: 32111.14.2025.0887

• 第二十七届中国科协年会学术论文 •    下一篇

融合机器学习技术的阈下抑郁神经生理机制及干预

刘永进1,†(), 杨雪1,†, 杜欣欣1, 嵇文麒1, 臧寅垠2, 官锐园3, 宋森4, 钱铭怡2, 牟文婷5()   

  1. 1清华大学计算机科学与技术系, 北京 100084
    2北京大学心理与认知科学学院, 北京 100871
    3北京大学医学人文学院医学心理学系, 北京 100191
    4清华大学脑与智能实验室
    5清华大学心理与认知科学系, 北京 100084
  • 收稿日期:2024-08-04 出版日期:2025-06-15 发布日期:2025-04-09
  • 通讯作者: 刘永进, E-mail: liuyongjin@tsinghua.edu.cn;
    牟文婷, E-mail: wmu@mail.tsinghua.edu.cn
  • 作者简介:

    †共同第一作者

  • 基金资助:
    国家自然科学基金(U2336214)

Neurophysiological mechanisms and interventions of subthreshold depression by integrating machine learning techniques

LIU Yongjin1,†(), YANG Xue1,†, DU Xinxin1, JI Wenqi1, ZANG Yinyin2, GUAN Ruiyuan3, SONG Sen4, QIAN Mingyi2, MU Wenting5()   

  1. 1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
    2School of Psychological and Cognitive Science, Peking University, Beijing 100871, China
    3Department of Medical Psychology, School of Health Humanities, Peking University, Beijing 100191, China
    4Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
    5Department of Psychology, Tsinghua University, Beijing 100084, China
  • Received:2024-08-04 Online:2025-06-15 Published:2025-04-09

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摘要：

抑郁症是阻碍国民心理健康的重要因素。阈下抑郁是抑郁发病前期重要阶段, 探究其神经生理机制及动态发展规律有助于预测抑郁发病和进行预防性干预。为突破既往将抑郁视为静态单一诊断结果的局限, 本文基于复杂动力系统理论, 通过多时程多模态机器学习方法, 探讨阈下抑郁症状与神经生理特征之间的密切关联及关键预测因子。其次, 通过纵向追踪及神经动力学网络模型探查吸引子状态及其对随后抑郁发病和特征转化的预测。最后, 探索认知行为疗法对阈下抑郁的预防性干预效果以及吸引子状态的预测作用。研究结果用以解析阈下抑郁的神经生物学独特性, 并为抑郁症早期识别和精准预防的方法研发提供新的思路。

关键词: 阈下抑郁, 吸引子状态, 认知行为疗法, 预防性干预, 多模态机器学习

Abstract:

Major Depressive Disorder (MDD) is a significant global public health threat for both national mental health, causing significant societal burden for both individuals and families. Subthreshold depression (StD) is an ultra-high-risk phase for MDD. Understanding its neurophysiological mechanisms and dynamic development patterns can help predict MDD onset and inform the development of novel and effective preventive interventions. To overcome the limitation of regarding depression as a static and single diagnosis, this study uses dynamical systems theory and machine learning techniques to explore the StD's neurophysiological mechanisms. By combining resting-state, task-state evaluations, and clinical interviews, it constructs a neurodynamic network model for StD with multi-modal data and machine learning, followed by cross-population and cross-individual validation.

Firstly, using multi-modal data, Study 1 explores the differences in symptoms and neurophysiological mechanisms among individuals with StD, healthy, and MDD individuals from a population perspective, and then, identifies critical predictive neurophysiological indicators of StD. By combining resting-state and task-state evaluations, as well as clinical interviews, it explores the distinctiveness of symptoms and multi-modal data in StD individuals (versus healthy and MDD). And the neurodynamic network model for StD will be constructed using multi-modal data combined with machine learning techniques to analyze depressive symptoms and neurophysiological characteristics, followed by cross-population and cross-individual validation. at the individual level, Study 2 use ecological momentary assessment combined with follow-up tracking to investigate the relationship between the attractor state of subthreshold depression and subsequent transition to MDD, along with accompanying neurophysiological changes. A dynamic development prediction model will be constructed. This study utilizes longitudinal tracking of multi-modal data combined with machine learning techniques to measure the attractor of StD and explore its predictive ability for subsequent depressive states and neurophysiological characteristic changes. Finally, to overcome the limitations of evaluating the preventive intervention effects for StD mainly relied on clinical diagnostic results, this study explores the effectiveness of cognitive behavioral therapy (CBT) in preventing the progression of StD from a commonality perspective, with the depressive symptoms and neurophysiological characteristics as objective evaluation indicators of intervention effectiveness. Furthermore, it elucidates the predictive role of the individual's attractor state in the future transformation of subthreshold depression. Study 3 uses a randomized controlled trial to investigate the preventive intervention efficacy of CBT group (versus waiting list group) on subthreshold depression and the mediating role of the attractor state. Based on the above study, this study proposes a predictive hypothesis regarding the preventive effect of cognitive behavioral therapy (CBT) on subthreshold depression based on the dynamical systems theory. It is hypothesized that CBT can reduce depressive symptoms and the transition rate by enhancing the stability of the attractor state, and this effect can be measured by neurophysiology characteristics.

In conclusion, this study offers a dynamic-systems-theory-driven approach, integrating multi-modal machine learning techniques to analyze the neurophysiological uniqueness of StD. This study helps develop more precise models for predicting StD and evaluating preventive intervention effects based on neurophysiological characteristics obtained from various sensors. These results can be used for daily physiological assessments, early detection of emotional or mental states earlier deterioration, and development of more precise early diagnosis and prevention strategies for MDD.

Key words: subthreshold depression, attractors, cognitive-behavioral therapy, preventive intervention, multi- modal machine learning

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