{
  "abstract": "Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or \\textit{fingerprints}, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, and limited in their ability to generalize across tasks and datasets. Methods: We propose a multiscale community-based functional connectome fingerprinting framework that characterizes each individual by the mesoscale structure of their functional networks. We introduce a signed multilayer community detection framework that incorporates both correlated and anti-correlated brain activity to identify subject-specific community structures across tasks and sessions. Graph-theoretic metrics are then computed from the resulting joint community structures to derive low-dimensional community-level fingerprint representations. Results: The proposed framework is evaluated on 810 healthy control subjects from the Human Connectome Project (HCP). The results show that community-based fingerprints provide a reliable and interpretable substrate for individualized brain characterization across sessions and tasks. Conclusion: Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints. Significance: The proposed framework offers a promising foundation for precision neuroimaging and personalized neuroscience applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.27483",
  "author_total": 2,
  "authors": [
    {
      "name": "Sema Athamnah"
    },
    {
      "name": "Selin Aviyente"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/62zr4skqe48my.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.27483",
  "fulltext_url": null,
  "id": "62zr4skqe48my",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.27483",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7204841023",
  "pdf_url": "https://arxiv.org/pdf/2608.27483",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/62zr4skqe48my.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:29Z",
      "native_id": "2608.27483",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.27483"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Multiscale Community-Based Fingerprinting of Signed Functional Networks",
  "title_text": "Multiscale Community-Based Fingerprinting of Signed Functional Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:29Z",
  "url": "https://research.draconitic.com/paper/62zr4skqe48my",
  "venue": "arXiv",
  "versions": []
}