[论文解读] Structural coarse-graining enables noise-robust functional connectivity and reveals hidden inter-subject variability

Izaro Fernandez-Iriondo, Antonio Jiménez-Marín+2 more|arXiv (Cornell University)|Feb 9, 2026

Functional Brain Connectivity Studies被引用 0

一句话总结

本文将基于扩散的结构粗化与谱噪声滤波相结合，从时限有限的 fMRI 数据中推导出可靠的、低维的功能网络，揭示比标准流程更广泛的跨被试变异性。

ABSTRACT

Functional connectivity estimates are highly sensitive to analysis choices and can be dominated by noise when the number of sampled time points is small relative to network dimensionality. This issue is particularly acute in fMRI, where scan resolution is limited. Because scan duration is constrained by practical factors (e.g., motion and fatigue), many datasets remain statistically underpowered for high-dimensional correlation estimation. We introduce a framework that combines diffusion-based structural coarse-graining with spectral noise filtering to recover statistically reliable functional networks from temporally limited data. The method reduces network dimensionality by grouping regions according to diffusion-defined communication. This produces coarse-grained networks with dimensions compatible with available time points, enabling random matrix filtering of noise-dominated modes. We benchmark three common FC pipelines against our approach. We find that raw-signal correlations are strongly influenced by non-stationary fluctuations that can reduce apparent inter-subject variability under limited sampling conditions. In contrast, our pipeline reveals a broader, multimodal landscape of inter-subject variability. These large-scale organization patterns are largely obscured by standard pipelines. Together, these results provide a practical route to reliable functional networks under realistic sampling constraints. This strategy helps separate noise-driven artifacts from reproducible patterns of human brain variability.

研究动机与目标

• 在高维度和有限时间点的 fMRI 中，为可靠的功能连接估计建立动机。
• 提出一个框架，通过扩散定义的结构通信来降低网络维度。
• 整合谱噪声滤波，以在粗化网络中抑制噪声支配的模态。
• 与常见的功能连接分析流程进行基准比较，以评估鲁棒性和变异性。

提出的方法

• 通过根据扩散定义的通信对区域进行分组，构建基于扩散的粗化网络。
• 应用谱噪声滤波，在保留有意义结构的同时去除噪声支配的模态。
• 使用时限数据将所提出的流程与三种常见的 FC 流程进行比较。
• 评估非平稳波动在不同流程下对跨被试变异性的影响。
• 分析粗化后的网络是否揭示在标准方法下被掩盖的多模态跨被试变异性。

实验结果

研究问题

• RQ1 扩散基于的结构粗化是否能产生对采样有限鲁棒的功能网络？
• RQ2 谱噪声滤波是否提高在短扫描时长下功能连接估计的可靠性？
• RQ3 与标准 FC 流程相比，所提出的方法对观察到的跨被试变异性有何影响？
• RQ4 使用粗化网络时，哪些跨被试变异模式得以出现，而传统方法掩盖了这些模式？

主要发现

• 在有限采样下，原始信号相关性受非平稳波动的强烈影响。
• 所提出的流程揭示了更广泛、多模态的跨被试变异性景观。
• 标准流程在很大程度上掩盖了变异性的规模化组织模式。
• 粗化网络在可用时间点范围内实现了与之相容的可靠估计。