Adolescent pornography addiction requires early detection based on objective neurobiological biomarkers because self-report is prone to subjective bias due to social stigma. Conventional machine learning has not been able to model dynamic functional connectivity of the brain that fluctuates temporally during addictive stimulus exposure. This study proposes a state-of-the-art Dynamic Spatio-Temporal Graph Neural Network (DST-GNN) that integrates Phase Lag Index (PLI)-based Graph Attention Network (GAT) for spatial modeling and Bidirectional Gated Recurrent Unit (BiGRU) for temporal dynamics. The dataset consists of 14 adolescents (7 addicted, 7 healthy) with 19-channel EEG across 9 experimental conditions. Leave-One-Subject-Out Cross Validation (LOSO-CV) evaluation shows F1-Score of 71.00%$\pm$12.10% and recall of 85.71%, a 104% improvement compared to baseline. Ablation study confirms temporal contribution of 21% and PLI graph construction of 57%. Frontal-central regions (Fz, Cz, C3, C4) are identified as dominant biomarkers with Beta contribution of 58.9% and Hjorth of 31.2%, while Cz-T7 connectivity is consistent as a trait-level biomarker for objective screening.

翻译：青少年色情成瘾的早期检测需要基于客观的神经生物学标志物，因为自我报告易受社会污名化影响而产生主观偏差。传统机器学习方法未能有效建模成瘾刺激暴露期间随时间波动的动态脑功能连接。本研究提出一种先进的动态时空图神经网络（DST-GNN），该网络整合了基于相位滞后指数（PLI）的图注意力网络（GAT）进行空间建模，以及双向门控循环单元（BiGRU）进行时序动态建模。数据集包含14名青少年（7名成瘾者，7名健康者）在9种实验条件下的19通道脑电信号。留一被试交叉验证（LOSO-CV）评估显示F1分数为71.00%$\pm$12.10%，召回率为85.71%，较基线提升104%。消融实验证实时序建模贡献度为21%，PLI图构建贡献度为57%。研究发现额中央区（Fz, Cz, C3, C4）是主要生物标志物，其中Beta节律贡献度58.9%，Hjorth参数贡献度31.2%，而Cz-T7连接性作为特质层面生物标志物在客观筛查中表现稳定。