{
  "abstract": "Cortical surface registration is often driven by local geometric descriptors (e.g., sulcal depth and curvature). While this approach achieves geometric correspondence, it neglects the long-range wiring constraints imposed by white-matter anatomy. Diffusion MRI tractography offers these crucial constraints; however, prior connectivity-informed pipelines typically align precomputed connectivity matrices, making the optimization highly sensitive to connectivity estimation and its resolution. In this paper, we introduce ConSEAL (Connectivity-Informed Streamline Endpoint Alignment), a novel connectivity-based surface registration method that aligns cortical surfaces by operating directly on white-matter fiber-tract endpoints. We model tract endpoints as a point cloud on the product manifold $Ω\\times Ω$, where $Ω$ represents the spherical domain of the inflated cortical hemispheres. Our alignment method iteratively (i) computes a small diffeomorphic warp for $Ω$ by minimizing connectivity mismatch, and (ii) updates the endpoints based on this warp. The method relies on a geometric framework that ensures output warps are diffeomorphisms and has a final goal that optimizes the matching of well-known fiber bundles. Experiments on Human Connectome Project (HCP) data demonstrate that ConSEAL improves tract-level correspondence, achieving higher connectivity-level overlap coefficients on major fiber bundles and stronger robustness across grid resolutions for $Ω$ compared to state-of-the-art methods such as ENCORE and MSMAll.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.16742",
  "author_total": 3,
  "authors": [
    {
      "name": "Yang Xiang"
    },
    {
      "name": "Martin Cole"
    },
    {
      "name": "Zhengwu Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/01ff7aydqsw3q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.16742",
  "fulltext_url": null,
  "id": "01ff7aydqsw3q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.16742",
  "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.CV"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2741|Advanced Neuroimaging Techniques and Applications"
    ]
  ],
  "openalex": "W7161730211",
  "pdf_url": "https://arxiv.org/pdf/2605.16742",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/01ff7aydqsw3q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:36Z",
      "native_id": "2605.16742",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.16742"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "ConSEAL: Connectivity-Informed Streamline Endpoint Alignment for Diffeomorphic Cortical Registration",
  "title_text": "ConSEAL: Connectivity-Informed Streamline Endpoint Alignment for Diffeomorphic Cortical Registration",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:36Z",
  "url": "https://research.draconitic.com/paper/01ff7aydqsw3q",
  "venue": "arXiv",
  "versions": []
}