[论文解读] Time-Varying Directed Interactions in Functional Brain Networks: Modeling and Validation

Nan Xu, Xiaodi Zhang+6 more|arXiv (Cornell University)|Feb 17, 2026

Functional Brain Connectivity Studies被引用 0

一句话总结

本文提出滑动窗口预测相关性（SWpC），在每个窗口中嵌入方向性线性时不变模型以估计时变有向功能连接性，并在多模态数据与临床背景下验证其有效性。

ABSTRACT

Understanding the dynamic nature of brain connectivity is critical for elucidating neural processing, behavior, and brain disorders. Traditional approaches such as sliding-window correlation (SWC) characterize time-varying undirected associations but do not resolve directional interactions, limiting inference about time-resolved information flow in brain networks. We introduce sliding-window prediction correlation (SWpC), which embeds a directional linear time-invariant (LTI) model within each sliding window to estimate time-varying directed functional connectivity (FC). SWpC yields two complementary descriptors of directed interactions: a strength measure (prediction correlation) and a duration measure (window-wise duration of information transfer). Using concurrent local field potential (LFP) and fMRI BOLD recordings from rat somatosensory cortices, we demonstrate stable directionality estimates in both LFP band-limited power and BOLD. Using Human Connectome Project (HCP) motor task fMRI, SWpC detects significant task-evoked changes in directed FC strength and duration and shows higher sensitivity than SWC for identifying task-evoked connectivity differences. Finally, in post-concussion vestibular dysfunction (PCVD), SWpC reveals reproducible vestibular-multisensory brain-state shifts and improves healthy-control vs subacute patient (HC-ST) discrimination using state-derived features. Together, these results show that SWpC provides biologically interpretable, time-resolved directed connectivity patterns across multimodal validation and clinical application settings, supporting both basic and translational neuroscience.

研究动机与目标

• 需要超越无向测度（如 SWC）的时分辨有向脑连接性。
• 介绍并形式化滑动窗口预测相关性（SWpC）作为有向FC估计量。
• 展示 SWpC 在多模态与临床数据集中的生物学可解释性与一致性。

提出的方法

• 在每个滑动窗口内嵌入一个方向性的线性时不变（LTI）模型，以估计时变的有向FC。
• 从 SWpC 中计算强度描述子（预测相关性）和持续时间描述子（信息传递在窗口内的持续时间）。
• 使用大鼠体感皮层的同时获取的 LFP 频带功率与 BOLD 信号验证方向性估计的生理 plausibility。
• 对人类连接组计划（HCP）运动任务 fMRI 应用 SWpC，以评估任务诱发的有向FC强度与持续时间的变化。
• 将 SWpC 与滑动窗口相关性（SWC）进行比较，以评估在检测任务诱导连通性差异方面的敏感性。

实验结果

研究问题

• RQ1 SWpC 能否在多模态脑数据中可靠恢复时变的有向相互作用？
• RQ2 SWpC 获得的方向性度量是否反映生理可行的信息流及任务诱导变化？
• RQ3 相比于 SWC，SWpC 是否在检测任务或临床条件差异方面更敏感？
• RQ4 SWpC 是否能在前庭功能障碍相关队列中揭示临床相关的脑状态转变？

主要发现

• SWpC 在 LFP 和 BOLD 信号中均可产出稳定的方向性估计。
• SWpC 在 HCP 运动任务中检测到有向FC强度与持续时间的显著任务诱导变化。
• SWpC 展现比 SWC 更高的灵敏度，用于识别任务诱导的连通性差异。
• 在前庭多感官疾病（PCVD）研究中，SWpC 揭示可重复的前庭-多感官脑状态转变，并通过状态衍生特征提高了健康对照与亚急性患者的区分度。