GLMdenoise improves multivariate pattern analysis of fMRI data

NeuroImage
Ian CharestKendrick N Kay

Abstract

GLMdenoise is a denoising technique for task-based fMRI. In GLMdenoise, estimates of spatially correlated noise (which may be physiological, instrumental, motion-related, or neural in origin) are derived from the data and incorporated as nuisance regressors in a general linear model (GLM) analysis. We previously showed that GLMdenoise outperforms a variety of other denoising techniques in terms of cross-validation accuracy of GLM estimates (Kay et al., 2013a). However, the practical impact of denoising for experimental studies remains unclear. Here we examine whether and to what extent GLMdenoise improves sensitivity in the context of multivariate pattern analysis of fMRI data. On a large number of participants (31 participants across 4 experiments; 3 T, gradient-echo, spatial resolution 2-3.75 mm, temporal resolution 1.3-2 s, number of conditions 32-75), we perform representational similarity analysis (Kriegeskorte et al., 2008a) as well as pattern classification (Haxby et al., 2001). We find that GLMdenoise substantially improves replicability of representational dissimilarity matrices (RDMs) across independent splits of each participant's dataset (average RDM replicability increases from r = 0.46 to r = 0.61). Additionally, ...Continue Reading

Citations

Feb 29, 2020·Journal of Cognitive Neuroscience·Chuanji GaoSvetlana V Shinkareva
Sep 9, 2020·Nature Methods·Kendrick KayKamil Uğurbil
Jul 31, 2020·Journal of Neurophysiology·Deborah A BaranyCathrin M Buetefisch
Nov 24, 2020·Computational Brain & Behavior·S Bobadilla-SuarezB C Love
Mar 9, 2021·Cortex; a Journal Devoted to the Study of the Nervous System and Behavior·Chuanji Gao, Svetlana V Shinkareva
Sep 22, 2021·Nature Communications·N Apurva Ratan MurtyNancy Kanwisher

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Methods Mentioned

BETA
RSA
MDS

Software Mentioned

MATLAB
FreeSurfer
MVPA
GLMdenoise
Crossnobis
RSA

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