{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.05913",
  "author_total": 3,
  "authors": [
    {
      "name": "Boseong Kim"
    },
    {
      "name": "Haejun Chung"
    },
    {
      "name": "Ikbeom Jang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4rmcb4n8c5hzj.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.05913",
  "fulltext_url": null,
  "id": "4rmcb4n8c5hzj",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.05913",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7220715381",
  "pdf_url": "https://arxiv.org/pdf/2610.05913",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4rmcb4n8c5hzj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:11Z",
      "native_id": "2610.05913",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.05913"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI",
  "title_text": "CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:11Z",
  "url": "https://research.draconitic.com/paper/4rmcb4n8c5hzj",
  "venue": "arXiv",
  "versions": []
}