Multiscale Community-Based Fingerprinting of Signed Functional Networks

2026-08-25

AI Summary

为解决功能连接组指纹识别中的噪声敏感和泛化能力有限问题，提出了一种基于多尺度社区结构的方法，利用图论度量来表征个体特征。

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.

Problem

• functional connectomes
• subject-specific signatures
• edge-level features
• noise sensitivity
• generalization

Innovation

• multiscale community-based
• functional connectome fingerprinting
• signed multilayer community detection
• graph-theoretic metrics