Functional connectivity (FC) analysis of resting-state fMRI data provides a framework for characterizing brain networks and their association with participant-level covariates. Due to the high dimensionality of neuroimaging data, standard approaches often average signals within regions of interest (ROIs), which ignores the underlying spatiotemporal dependence among voxels and can lead to biased or inefficient inference. We propose to use a summary statistic -- the empirical voxel-wise correlations between ROIs -- and, crucially, model the complex covariance structure among these correlations through a new positive definite covariance function. Building on this foundation, we develop a computationally efficient two-step estimation procedure that enables statistical inference on covariate effects on region-level connectivity. Simulation studies show calibrated uncertainty quantification, and substantial gains in validity of the statistical inference over the standard averaging method. With data from the Autism Brain Imaging Data Exchange, we show that autism spectrum disorder is associated with altered FC between attention-related ROIs after adjusting for age and gender. The proposed framework offers an interpretable and statistically rigorous approach to estimation of covariate effects on FC suitable for large-scale neuroimaging studies.

翻译：静息态fMRI数据的功能连接分析为刻画脑网络及其与受试者层面协变量的关联提供了框架。由于神经影像数据的高维特性，标准方法通常在感兴趣区域内平均信号，这忽略了体素间潜在的时空依赖性，可能导致有偏或低效的统计推断。我们提出使用一种汇总统计量——ROI间经验性体素水平相关性，并通过一种新的正定协方差函数对这些相关性间的复杂协方差结构进行建模。在此基础上，我们开发了一种计算高效的两步估计方法，能够对区域水平连接度的协变量效应进行统计推断。模拟研究表明该方法具有校准良好的不确定性量化能力，且在统计推断有效性上较标准平均方法有显著提升。通过自闭症脑影像数据交换计划的数据，我们发现经年龄和性别校正后，自闭症谱系障碍与注意相关ROI间功能连接的改变存在关联。所提出的框架为大规模神经影像研究中协变量对功能连接影响的估计提供了一种可解释且统计严谨的方法。