发表机构
Hanyang University; Hankuk University of Foreign Studies（汉阳大学; 韩国外国语大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MovieSTAGE通过结合场景内超图FC、场景间转换差异和整部电影FC，在CMI-HBN队列的ADHD分类中取得最高平均性能，验证了事件对齐表征的增量价值。
AI 中文摘要
自然情境下的电影fMRI提供了一种共享的、具有时间结构的脑动力学探测手段，然而预测模型通常依赖于全时程功能连接（FC）或与叙事事件不对齐的时间通用表征。我们提出了MovieSTAGE（场景、转换与全局编码），一个多尺度框架，结合了场景内基于超图结构的FC图谱组织、相邻场景间的无符号FC图谱差异以及整部电影的FC。我们使用CMI-HBN《神偷奶爸》队列中的260名参与者，通过10次重复的分层五折交叉验证、完整的折外（OOF）预测以及配对受试者聚类自助法和置换检验，评估了病例对照、ADHD亚型和三分类任务。MovieSTAGE分别取得了0.69、0.73和0.75的AUROC，以及67.6%、69.8%和58.3%的平衡准确率，在所评估的方法中获得了最高的平均点估计。在三分类任务中，完整模型优于所有两分支变体；在匹配设置下，HGNN场景编码器优于MLP、GAT和BNT替代方案；人工标注的分区优于时长匹配的随机和固定计数GSBS对照。这些受控结果支持在该队列中，事件对齐的场景和转换表征与整部电影FC结合时具有增量预测价值。事后模型衍生的分析产生了涉及额顶叶和默认模式系统的网络级假设。
英文摘要
Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional connectivity (FC) or temporally generic representations that are not aligned with narrative events. We introduce MovieSTAGE (Scene, Transition, and Global Encoding), a multiscale framework that combines hypergraph-structured FC-profile organization within scenes, unsigned FC-profile differences across adjacent scenes, and whole-movie FC. We evaluated 260 participants from the CMI-HBN Despicable Me cohort on case-control, ADHD-subtype, and three-class classification using 10 repetitions of stratified five-fold cross-validation, complete out-of-fold (OOF) predictions, and paired subject-cluster bootstrap and permutation tests. MovieSTAGE achieved AUROCs of 0.69, 0.73, and 0.75 and balanced accuracies of 67.6%, 69.8%, and 58.3%, respectively, yielding the highest mean point estimates among the evaluated methods. On the three-class task, the full model outperformed all two-branch variants, the HGNN scene encoder outperformed MLP, GAT, and BNT alternatives under matched settings, and the human-annotated partition outperformed duration-matched random and fixed-count GSBS controls. These controlled results support incremental predictive value from event-aligned scene and transition representations when combined with whole-movie FC in this cohort. Post-hoc model-derived analyses generated network-level hypotheses involving frontoparietal and default-mode systems.
CommentsAccepted to the 2026 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2026)
