%A Davey,Catherine E.
%A Grayden,David B.
%A Johnston,Leigh A.
%D 2021
%J Frontiers in Neuroscience
%C
%F
%G English
%K power,resting-state,Correlation,connectivity,fMRI,non-stationarity
%Q
%R 10.3389/fnins.2021.574979
%W
%L
%M
%P
%7
%8 2021-February-24
%9 Methods
%#
%! Correcting for non-stationarity
%*
%<
%T Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses
%U https://www.frontiersin.org/article/10.3389/fnins.2021.574979
%V 15
%0 JOURNAL ARTICLE
%@ 1662-453X
%X 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. Theoretical 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 characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.