Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric fMRI Paradigms II: A Method to Obtain First Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

Brain Connect. 2017 Nov 22;:

Authors: Gopinath K, Krishnamurthy V, Lacey S, Sathian K

Abstract
A recent study (Eklund et al., 2016) has shown that cluster-wise family-wise error (FWE) rate corrected inferences made in parametric statistical methods based fMRI studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggests otherwise (Eklund et al., 2016). Hence the residuals from general linear model (GLM) based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF (Eklund et al., 2016). Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residuals time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad-hoc stochastic colored noise models. Further the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

PMID: 29161884 [PubMed - as supplied by publisher]