跳到正文
arXiv:cs.AI· Ying Xu, Xiaojun Liang, Li Zhang, Yixuan Yuan, Gan Huang, Yongjie Zhou, Zhen Liang·· 4 小时前

社交疼痛扰乱非自杀性自伤青少年的情绪-行动脑状态动态

Social Pain Disrupts Emotion-Action Brain-State Dynamics in Adolescents with Non-Suicidal Self-Injury

AI 导读

研究结合疼痛实验范式、EEG 微状态分析和可解释深度序列模型,对106名抑郁青少年(67名伴非自杀性自伤 DN+、39名不伴 DN-)在社交疼痛、生理疼痛与静息态下进行神经动力学分析。

正文

View PDF HTML (experimental)

Abstract:Non-suicidal self-injury (NSSI) is prevalent among adolescents with depression, but the rapid brain-state dynamics linking social distress to maladaptive behavior remain unclear. We combine an experimental pain paradigm, electroencephalography (EEG) microstate analysis, and interpretable deep sequence modeling to investigate NSSI-related neurodynamics in 106 adolescents with depression, including 67 with NSSI (DN+) and 39 without NSSI (DN-), during social pain, physical pain, and resting-state conditions. A model integrating disease-specific, domain-adversarial, consistency, and contrastive learning captures higher-order dependencies in microstate sequences. Social pain yields the strongest NSSI discrimination, with 68.55% accuracy, outperforming the best baseline by 8.94% points. Model interpretation and conventional microstate analyses reveal weakened bidirectional transitions between MS3 and MS5 in DN+ adolescents during social pain. Source reconstruction associates MS3 with emotional/interoceptive processing and MS5 with action preparation, suggesting disrupted emotion-action coupling. Time-resolved analyses show greater early-to-middle action-state recruitment and later emotion-state recruitment in DN+ adolescents. In DN- adolescents, MS5-to-MS3 dynamics mediate associations between social-evaluation sensitivity and affective outcomes, whereas this mediation is absent in DN+; conversely, MS3-to-MS5 transitions are associated with greater negative affect in DN+. Together, these findings identify disrupted emotion-action coupling as a key neurodynamic mechanism underlying altered social pain processing in adolescents with NSSI, providing a mechanistically interpretable neural signature for objective identification of NSSI.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.11155 [cs.AI]
  (or arXiv:2610.11155v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.11155

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zhen Liang Jane [view email]
[v1] Thu, 8 Oct 2026 03:16:45 UTC (3,190 KB)

来源:arXiv:cs.AI · arxiv.org