基于多尺度社区的符号功能网络指纹识别
Multiscale Community-Based Fingerprinting of Signed Functional Networks
AI总结：
该研究针对现有功能连接组指纹识别方法泛化性差的问题，提出基于多尺度社区的符号多层社区检测框架，在HCP的810名被试上验证了社区级指纹可实现跨会话任务的可靠个体化脑表征。
AI中文摘要：
目的：近期研究表明，功能连接组包含被试特有的特征，即“指纹”，可跨重复会话和任务识别个体。现有方法大多依赖边级特征，这类特征对噪声敏感、难以解释，且跨任务和数据集的泛化能力有限。方法：我们提出一种基于多尺度社区的功能连接组指纹识别框架，通过功能网络的中尺度结构表征每个个体。我们引入一种符号多层社区检测框架，该框架整合正相关与负相关的脑活动，以识别跨任务和会话的被试特有社区结构。随后从所得联合社区结构中计算图论指标，以推导低维社区级指纹表征。结果：在来自人类连接组项目（HCP）的810名健康对照被试上评估所提框架。结果显示，社区级指纹为跨会话和任务的个体化脑表征提供了可靠且可解释的基础。结论：中尺度社区结构提供了有意义且具区分性的被试特有指纹。意义：所提框架为精准神经成像和个性化神经科学应用提供了有前景的基础。
英文摘要：
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.
