  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.
