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
CoHyFuse提出条件感知超图框架，通过构建任务状态特异的ROI超边并融合全会话FC，在任务态fMRI预测中超越基线，提供可解释的状态解析连接组视图。
AI 中文摘要
任务态fMRI连接组揭示了状态依赖的神经重构，然而传统方法通过将不同条件聚合为静态成对图来边缘化这些信号，从而掩盖了条件特异的多ROI组织。我们引入了CoHyFuse，一个条件感知的、以ROI为中心的超图框架，该框架从条件态功能连接（FC）配置文件嵌入构建任务状态特异的关联矩阵，允许同一ROI在不同任务阶段形成不同的多ROI超边。条件特异的邻域大小$K_q$进一步使超边尺度适应每个任务状态，所得条件嵌入与互补的全会话FC分支融合以进行预测。在AABC队列（N=1,074）中，CoHyFuse在FACENAME流体认知综合（FCC）预测（7.83±0.10 MAE，0.439±0.026 R²）和VISMOTOR年龄预测（7.52±0.37 MAE，0.592±0.022 R²）上取得了评估基线中最佳的平均折外预测性能。在辅助的CMI-HBN注意力缺陷/多动障碍（ADHD）分类基准（N=223）中，CoHyFuse获得了72.0±2.1%的宏AUC和74.2±2.9%的准确率。消融研究支持条件态关联构建和双视图融合的贡献，表明状态解析的ROI集结构提供了超越全会话FC的互补预测信息。遮挡分析将Distraction条件识别为模型预测的主要驱动因素，指向Salience/Ventral Attention Network（SAN）-FrontoParietal Network（FPN）以及SAN内部超边定义的ROI集基序作为候选模型相关模式。该框架为下游队列分析提供了可解释的、状态解析的连接组视图。
英文摘要
Task-fMRI connectomes reveal state-dependent neural reconfigurations, yet conventional methods marginalize these signals by aggregating distinct conditions into static pairwise graphs, thereby obscuring condition-specific multi-ROI organization. We introduce CoHyFuse, a condition-aware ROI-centered hypergraph framework that constructs a task-state-specific incidence matrix from condition-wise functional connectivity (FC)-profile embeddings, allowing the same ROI to form different multi-ROI hyperedges across task phases. Condition-specific neighborhood sizes $K_q$ further adapt the hyperedge scale to each task state, and the resulting condition embeddings are fused with a complementary whole-session FC branch for prediction. In the AABC cohort (N=1,074), CoHyFuse achieved the best mean out-of-fold predictive performance among evaluated baselines on FACENAME Fluid Cognition Composite (FCC) prediction (7.83$\pm$0.10 MAE, 0.439$\pm$0.026 \(R^2\)) and VISMOTOR age prediction (7.52$\pm$0.37 MAE, 0.592$\pm$0.022 \(R^2\)). In an auxiliary CMI-HBN attention-deficit/hyperactivity disorder (ADHD) classification benchmark (N=223), CoHyFuse obtained 72.0$\pm$2.1\% macro-AUC and 74.2$\pm$2.9\% accuracy. Ablation studies support the contributions of condition-wise incidence construction and dual-view fusion, suggesting that state-resolved ROI-set structure provides complementary predictive information beyond whole-session FC alone. Occlusion analysis identifies the Distraction condition as the primary driver of model prediction, pointing toward the Salience/Ventral Attention Network (SAN)--FrontoParietal Network (FPN) and within-SAN hyperedge-defined ROI-set motifs as candidate model-relevant patterns. This framework provides an interpretable, state-resolved view of the connectome for downstream cohort analysis.
CommentsAccepted to the 2026 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2026)
