{
  "abstract": "Whole brain tractography contains rich trajectory-level information about white matter organization, yet conventional structural connectomes capture only part of it. We introduce Structural Network Coupling (SNC), a network representation enabled by SILICA (Streamline Independent Component Analysis). SILICA decomposes tractography into structural source components and their streamline-specific loading profiles, while SNC characterizes relationships among these components through their shared trajectory expression. The resulting component-level network retains direct correspondence to the underlying white-matter pathways and supports network visualization and graph-based analysis. SNC produced anatomically interpretable coupling among trajectory-resolved white-matter components and revealed a highly organized group architecture that independently recapitulated commissural, projection, and association systems. It also showed strong hemispheric segregation and structured cross-system coupling, and was highly stable across split-half samples. SNC extends SILICA from trajectory decomposition to network-level organization and provides a flexible framework for future structural and multimodal network analysis.",
  "abstract_shown": true,
  "affiliations": [
    "Georgia State University, Georgia Institute of Technology, Emory University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "L. Wu"
    },
    {
      "name": "V. Calhoun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/610pg495g510r.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.23.753938",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753938v1.full",
  "id": "610pg495g510r",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753938v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2741|Advanced Neuroimaging Techniques and Applications"
    ]
  ],
  "openalex": "W7214718886",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753938v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/610pg495g510r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:15Z",
      "native_id": "10.64898/2026.09.23.753938",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753938v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "SILICA-SNC: FROM STREAMLINES TO NETWORKS",
  "title_text": "SILICA-SNC: FROM STREAMLINES TO NETWORKS",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:15Z",
  "url": "https://research.draconitic.com/paper/610pg495g510r",
  "venue": "bioRxiv",
  "versions": []
}