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


Autoři: Yichen Li aff001;  Rebecca Saxe aff003;  Stefano Anzellotti aff004
Působiště autorů: Courant Institute of Mathematical Sciences, New York University, New York, NY, United States of America aff001;  Department of Computer Science, New York University, New York, NY, United States of America aff002;  Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America aff003;  Department of Psychology, Boston College, Chestnut Hill, MA, United States of America aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222914

Souhrn

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).

Klíčová slova:

Central nervous system – Forecasting – Functional magnetic resonance imaging – Noise reduction – Preprocessing – Principal component analysis – Simulation and modeling – Statistical data


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PLOS One


2019 Číslo 9

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