AION
Research paperInterpretability · Robotics & Embodied AI · Large Language Models1 source · Oct 8, 2026

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

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.

Key points

  • 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.
  • Model interpretation and conventional microstate analyses reveal weakened bidirectional transitions between MS3 and MS5 in DN+ adolescents during social pain.
  • Time-resolved analyses show greater early-to-middle action-state recruitment and later emotion-state recruitment in DN+ adolescents.
  • 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.

Sources (1)

  • [1]Social Pain Disrupts Emotion-Action Brain-State Dynamics in Adolescents with Non-Suicidal Self-Injury
    arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:16 AM
    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.

Extractive summary: sentences quoted from the sources.

Before this

  1. Oct 7, 2026Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Miss
  2. Oct 7, 2026MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition
  3. Oct 7, 2026An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
  4. Oct 7, 2026Node-level Graph Neural Architecture Search Framework
  5. Oct 6, 2026PVSync: A Unified Lip-Sync Expert for Timing and Articulation
  6. Oct 6, 2026Contrastive Learning for Aspect Representation towards Explainable Recommendation

Related