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

doi:10.3724/SP.J.1042.2025.0887

Abstract

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

CLC Number:

R395

LIU Yongjin, YANG Xue, DU Xinxin, JI Wenqi, ZANG Yinyin, GUAN Ruiyuan, SONG Sen, QIAN Mingyi, MU Wenting. Neurophysiological mechanisms and interventions of subthreshold depression by integrating machine learning techniques[J]. Advances in Psychological Science, 2025, 33(6): 887-904.

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