[
    {
        "id": "osp-17128",
        "type": "article-journal",
        "title": "CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI",
        "author": [
            {
                "family": "Kim",
                "given": "Boseong"
            },
            {
                "family": "Chung",
                "given": "Haejun"
            },
            {
                "family": "Jang",
                "given": "Ikbeom"
            }
        ],
        "URL": "https://omanscience.com/en/articles/cohyfuse-condition-wise-hypergraph-fusion-with-global-connectome-in-task-fmri",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]