用单通道脑电和图神经网络，发现自残时大脑反馈回路异常反转。
A Graph Neural Network based on a Functional Topology Model: Unveiling the Dynamic Mechanisms of Non-Suicidal Self-Injury in Single-Channel EEG
- 基于功能能量拓扑模型构建理论驱动的图神经网络。
- 跨被试准确率达73.7%，单被试准确超85%。
- 揭示自残时躯体反馈回路失效，呈现无效休眠状态。
本研究提出并初步验证了一种新型“功能-能量拓扑模型”，利用图神经网络（GNN）从真实场景下的单通道脑电数据中解码非自杀性自伤（NSSI）的神经动态机制。通过手机应用和便携式Fp1脑电头戴设备，对三名青少年在冲动与非冲动状态下持续约一个月的脑电数据进行采集。构建了一个包含七个功能节点的理论驱动型GNN，采用被试内（80/20划分）与留一被试外交叉验证（LOSOCV）评估性能，并使用GNNExplainer实现可解释性分析。结果显示，模型在被试内测试中准确率超过85%，跨被试表现显著高于随机水平（约73.7%）。可解释性分析揭示关键发现：在NSSI状态下，调控躯体感觉的关键反馈回路出现功能障碍及方向反转——大脑丧失通过负向身体反馈自我校正的能力，调节机制进入“无效休眠”状态。该研究证明了理论引导的GNN在稀疏、单通道脑电数据上的可行性，所识别的‘反馈回路反转’为NSSI提供了新颖、动态且可计算的机制模型，有望推动客观生物标志物与下一代数字疗法的发展。
原文摘要 · Abstract (English)
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).
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