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

AI模型识别青少年自残的神经动力学标记

研究人员开发了一种深度序列建模方法,用于分析患有抑郁症和非自杀性自伤(NSSI)的青少年的大脑状态动力学。该研究对106名青少年进行了脑电图(EEG)微状态分析,区分了在社交痛苦、身体痛苦和休息状态下有NSSI和无NSSI的青少年。该模型在识别NSSI方面达到了68.55%的准确率,比基线方法提高了近9%。研究结果表明,在社交痛苦期间,患有NSSI的青少年在情绪处理(MS3)和行动准备(MS5)之间的耦合存在中断。 AI

影响 这项研究展示了AI在识别精神健康状况的细微神经动力学标记方面的潜力,可能有助于客观诊断和干预。

排序理由 学术论文,详细介绍了一种新颖的基于AI的分析与心理健康相关的神经动力学的方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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AI模型识别青少年自残的神经动力学标记

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学术论文,详细介绍了一种新颖的基于AI的分析与心理健康相关的神经动力学的方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ying Xu, Xiaojun Liang, Li Zhang, Yixuan Yuan, Gan Huang, Yongjie Zhou, Zhen Liang ·

    社会痛苦扰乱非自杀性自伤青少年情绪-行为大脑状态动力学

    arXiv:2610.11155v1 Announce Type: new 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, electroence…