基于功能拓扑模型的图神经网络：揭示单通道脑电图中非自杀性自伤行为的动态机制
A Graph Neural Network based on a Functional Topology Model: Unveiling the Dynamic Mechanisms of Non-Suicidal Self-Injury in Single-Channel EEG
AI总结：
本研究提出功能-能量拓扑模型并结合图神经网络，从单通道脑电中解码非自杀性自伤状态，发现关键反馈回路方向反转及“无效空转”机制，为客观生物标志物和数字疗法奠定基础。
摘要：
目的：本研究提出并初步验证了一种新颖的“功能-能量拓扑模型”，旨在揭示非自杀性自伤（NSSI）的神经动力学机制，利用图神经网络（GNNs）从真实环境下的单通道脑电图中解码脑网络模式。方法：使用智能手机应用和便携式Fp1脑电头带，在约1个月时间内采集了三名患有NSSI的青少年在冲动状态和非冲动状态下的脑电数据。构建了一个由七个功能节点组成的理论驱动GNN。通过被试内（80/20划分）和留一被试交叉验证（LOSOCV）评估性能。使用GNNExplainer进行可解释性分析。结果：该模型取得了较高的被试内准确率（>85%）和显著高于随机水平的跨被试性能（约73.7%）。可解释性分析揭示了一个关键发现：在NSSI状态下，一个调节躯体感觉的关键反馈回路表现出功能障碍和方向反转。具体而言，大脑失去了通过负性身体反馈进行自我纠正的能力，调节机制进入“无效空转”状态。结论：本工作证明了将理论引导的GNN应用于稀疏单通道脑电图以解码复杂心理状态的可行性。所识别的“反馈回路反转”为NSSI机制提供了一种新颖、动态且可计算的模型，为客观生物标志物和下一代数字疗法（DTx）铺平了道路。
英文摘要：
Objective: This study proposes and preliminarily validates a novel "Functional-Energetic Topology Model" to uncover neurodynamic mechanisms of Non-Suicidal Self-Injury (NSSI), using Graph Neural Networks (GNNs) to decode brain network patterns from single-channel EEG in real-world settings.Methods: EEG data were collected over ~1 month from three adolescents with NSSI using a smartphone app and a portable Fp1 EEG headband during impulsive and non-impulsive states. A theory-driven GNN with seven functional nodes was built. Performance was evaluated via intra-subject (80/20 split) and leave-one-subject-out cross-validation (LOSOCV). GNNExplainer was used for interpretability.Results: The model achieved high intra-subject accuracy (>85%) and significantly above-chance cross-subject performance (approximately73.7%). Explainability analysis revealed a key finding: during NSSI states, a critical feedback loop regulating somatic sensation exhibits dysfunction and directional reversal. Specifically, the brain loses its ability to self-correct via negative bodily feedback, and the regulatory mechanism enters an "ineffective idling" state.Conclusion: This work demonstrates the feasibility of applying theory-guided GNNs to sparse, single-channel EEG for decoding complex mental states. The identified "feedback loop reversal" offers a novel, dynamic, and computable model of NSSI mechanisms, paving the way for objective biomarkers and next-generation Digital Therapeutics (DTx).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
