Functional magnetic resonance imaging (fMRI) is a neuroimaging technique known for its ability to capture brain activity non-invasively and at fine spatial resolution (2-3mm). Cortical surface fMRI (cs-fMRI) is a recent development of fMRI that focuses on signals from tissues that have neuronal activities, as opposed to the whole brain. cs-fMRI data is plagued with non-stationary spatial correlations and long temporal dependence which, if inadequately accounted for, can hinder downstream statistical analyses. We propose a fully integrated approach that captures both spatial non-stationarity and varying ranges of temporal dependence across regions of interest. More specifically, we impose non-stationary spatial priors on the latent activation fields and model temporal dependence via fractional Gaussian errors of varying Hurst parameters, which can be studied through a wavelet transformation and its coefficients' variances at different scales. We demonstrate the performance of our proposed approach through simulations and an application to a visual working memory task cs-fMRI dataset.

翻译：功能磁共振成像（fMRI）是一种能够以高空间分辨率（2-3毫米）无创捕获大脑活动的神经影像学技术。皮层表面fMRI（cs-fMRI）是fMRI的最新进展，它聚焦于具有神经活动的组织信号（而非全脑信号）。cs-fMRI数据存在非平稳空间相关性和长程时间依赖性问题，若未充分处理，将阻碍后续统计分析。我们提出一种完全集成的方法，既能捕获空间非平稳性，又能表征感兴趣区域间不同范围的时间依赖性。具体而言，我们在潜在激活场上施加非平稳空间先验，并通过具有不同赫斯特参数的分数高斯误差来建模时间依赖性——该误差可通过小波变换及其不同尺度下的系数方差进行研究。我们通过模拟实验和视觉工作记忆任务cs-fMRI数据集的实证应用，展示了所提方法的性能。