Author(s)
Ian Charest
Published 2 Projects
Neuroscience Neuroimaging Representational Similarity High Resolution Fmri Bold Fmri
Nikolaus Kriegeskorte
Published 3 Projects
Neuroscience Human Visual Cortex Intrinsic Dynamics Representational Similarity Pattern Information
Kendrick Kay
Published 5 Projects
Neuroscience Representational Similarity Vasculature Veins Bold Fmri
Content
Video Abstract (AI generated) (01:31) Paper Preprint NeuroImageGLMdenoise 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.
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Kendrick Kay. (2021, Nov 1).GLMdenoise improves multivariate pattern analysis of fMRI data[Video]. Scitok. https://scitok.com/project/p/409d6c3e
Charest Ian. "GLMdenoise improves multivariate pattern analysis of fMRI data" Scitok, uploaded by Kay Kendrick, 1 Nov, 2021, https://scitok.com/project/p409d6c3e
Kendrick Kay. "GLMdenoise improves multivariate pattern analysis of fMRI data" Scitok. (Nov 1, 2021). https://scitok.com/project/p/409d6c3e
Kendrick Kay (Nov 1, 2021). GLMdenoise improves multivariate pattern analysis of fMRI data Scitok. https://scitok.com/project/p/409d6c3e
Kendrick Kay. GLMdenoise improves multivariate pattern analysis of fMRI data[video]. 2021 Nov 1. https://scitok.com/project/p/409d6c3e
@online{al2006link, title={ GLMdenoise improves multivariate pattern analysis of fMRI data }, author={ Kay, Kendrick }, organization={Scitok}, month={ Nov }, day={ 1 }, year={ 2021 }, url = {https://scitok.com/project/p/409d6c3e}, }