Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses - Davey, Catherine E.; Grayden, David B.; Johnston, Leigh A. (Frontiers in Neuroscience, 2021)
In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice- dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time- varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoreti- cal results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterised by an inverse Gamma distribution. The resulting distribution of a voxel’s signal intensity is analytically derived to be a generalised Student’s-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.