Multiscale Community-Based Fingerprinting of Signed Functional Networks

[2608.27483] Multiscale Community-Based Fingerprinting of Signed Functional Networks Skip to main content Search Submit Donate Log in Quantitative Biology > Neurons and Cognition arXiv:2608.27483 (q-bio) [Submitted on 25 Aug 2026] Title: Multiscale Community-Based Fingerprinting of Signed Functional Networks Authors: Sema Athamnah , Selin Aviyente View PDF HTML (experimental) 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. Subjects: Neurons and Cognition (q-bio.NC) ; Machine Learning (cs.LG); Signal Processing (eess.SP) Cite as: arXiv:2608.27483 [q-bio.NC] (or arXiv:2608.27483v1 [q-bio.NC] for this version) https://doi.org/10.48550/arXiv.2608.27483 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sema Athamnah [ view email ] [v1] Tue, 25 Aug 2026 19:10:52 UTC (38,029 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.NC < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG eess eess.SP q-bio References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• Multiscale Community-Based Fingerprinting of Signed Functional Networks#779425
• Microfluidic platforms for probing spontaneous functional recovery in hierarchically modular neuronal networks.#465283
• “More research being done is so validating”: Insights from transgender and gender-diverse participants on a psychophysiological study with deception#613640
• “More research being done is so validating”: Insights from transgender and gender-diverse participants on a psychophysiological study with deception#613564
• Adaptively Truncated Signature-based Logistic Regression for Semi-parametric Functional Classification#1128507