GLMdenoise improves multivariate pattern analysis of fMRI data

Ian Charest, Nikolaus Kriegeskorte, Kendrick N. Kay

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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, we find that GLMdenoise substantially improves pairwise classification accuracy (average classification accuracy increases from 79% correct to 84% correct). We show that GLMdenoise often improves and never degrades performance for individual participants and that GLMdenoise also improves across-participant consistency. We conclude that GLMdenoise is a useful tool that can be routinely used to maximize the amount of information extracted from fMRI activity patterns.

Original languageEnglish (US)
Pages (from-to)606-616
Number of pages11
StatePublished - Dec 2018

Bibliographical note

Funding Information:
We thank J. Winawer for helpful comments. This work was supported by a European Research Council Starting Grant ERC-2017-StG 759432 (to I.C.), by a UK Medical Research Council Grant MC-A060-5PR60 and an ERC Starting Grant ERC-2010-StG 261352 (to N.K.), and by the McDonnell Center for Systems Neuroscience and Arts & Sciences at Washington University (to K.K.).

Publisher Copyright:
© 2018


  • Classification
  • Correlated noise
  • Cross-validation
  • Decoding
  • Denoising
  • General linear model
  • Multivariate pattern analysis
  • Representational similarity analysis

Center for Magnetic Resonance Research (CMRR) tags

  • IRP


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