Intersubject MVPD: Empirical comparison of fMRI denoising methods for connectivity analysis

Abstract

Noise is a major challenge for the analysis of fMRI data in general and for connectivity analyses in particular. As researchers develop increasingly sophisticated tools to model statistical dependence between the fMRI signal in different brain regions, there is a risk that these models may increasingly capture artifactual relationships between regions, that are the result of noise. Thus, choosing optimal denoising methods is a crucial step to maximize the accuracy and reproducibility of connectivity models. Most comparisons between denoising methods require knowledge of the ground truth: of what is the ‘real signal’. For this reason, they are usually based on simulated fMRI data. However, simulated data may not match the statistical properties of real data, limiting the generalizability of the conclusions. In this article, we propose an approach to evaluate denoising methods using real (non-simulated) fMRI data. First, we introduce an intersubject version of multivariate pattern dependence (iMVPD) that computes the statistical dependence between a brain region in one participant, and another brain region in a different participant. iMVPD has the following advantages: 1) it is multivariate, 2) it trains and tests models on independent partitions of the real fMRI data, and 3) it generates predictions that are both between subjects and between regions. Since whole-brain sources of noise are more strongly correlated within subject than between subjects, we can use the difference between standard MVPD and iMVPD as a ‘discrepancy metric’ to evaluate denoising techniques (where more effective techniques should yield smaller differences). As predicted, the difference is the greatest in the absence of denoising methods. Furthermore, a combination of removal of the global signal and CompCorr optimizes denoising (among the set of denoising options tested).

PyMVPD: A Toolbox for Multivariate Pattern Dependence

“…Note that however this procedure does not remove the need for denoising methods: some sources of noise can produce shared effects across multiple regions. A previous study investigated the effectiveness of different denoising techniques for MVPD (Li et al, 2019 ); among the techniques tested, CompCor (Behzadi et al, 2007 ) was the most effective, therefore we used CompCor for denoising in this study.…”

“…The number of participants needed for MVPD analysis might vary depending on the brain regions that are being investigated. In previous studies, numbers of participants similar to the ones used for MVPA have produced robust results (Anzellotti et al, 2017a;Li et al, 2019). Based on these considerations, in the present work we used the StudyForrest dataset (Hanke et al, 2016), a publicly available dataset that has been used in several MVPA studies.…”

“…The toolbox offers a set of functions for performing MVPD analyses, organized around a simple workflow. It also includes example Python scripts for several MVPD models, including linear regression models that were used in previous MVPD publications (Anzellotti et al, 2017a ; Li et al, 2019 ), and new models based on artificial neural networks. The models are accompanied by algorithms that can be used to evaluate their performance.…”

“…Among multivariate approaches to study the interactions between brain regions, multivariate pattern dependence (MVPD, Anzellotti et al, 2017a ; Li et al, 2019 ) is unique in that it trains and tests models of the interactions between brain regions using independent subsets of data, evaluating out-of-sample generalization. Like multivariate distance correlation, MVPD can be applied to both task data and resting state data.…”

Computational Fingerprints: Modeling Interactions Between Brain Regions as Points in a Function Space

“…These data consist of fMRI scans of 15 subjects (6 female, ages 21-39, mean = 29.4) as they watched the movie Forrest Gump . Data from one subject were removed from the analyses after failing the fMRIPrep preprocessing procedure [20] see also [21].…”