Paper Info
Title
Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms I: Revisiting Cluster-Based Inferences.
Abstract
In a recent study, Eklund et al. employed resting-state functional magnetic resonance imaging data as a surrogate for null functional magnetic resonance imaging (fMRI) datasets and posited that cluster-wise family-wise error (FWE) rate-corrected inferences made by using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; this was principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise. Here, we show that accounting for non-Gaussian signal components such as those arising from resting-state neural activity as well as physiological responses and motion artifacts in the null fMRI datasets yields first-and second-level general linear model analysis residuals with nearly uniform and Gaussian sACF. Further comparison with nonparametric permutation tests indicates that cluster-based FWE corrected inferences made with Gaussian spatial noise approximations are valid.
Year
DOI
Venue
2018
10.1089/brain.2017.0521
BRAIN CONNECTIVITY
Keywords
Field
DocType
cluster-based family-wise error rate calculation,fMRI parametric methods,general linear model residuals,Monte Carlo simulation,principal component analysis,spatial autocorrelation function,thresholding
Spatial analysis,Accounting,Spatial correlation,Functional magnetic resonance imaging,General linear model,Nonparametric statistics,Parametric statistics,Gaussian,Medicine,Principal component analysis
Journal
Volume
Issue
ISSN
8
1
2158-0014
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Kaundinya Gopinath1375.29
Venkatagiri Krishnamurthy211.04
K Sathian315412.56