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).
Highlights
Cognitive tasks elicit the activation of multiple brain regions [1, 2]
We compared the effectiveness of four different denoising approaches for fMRI data: regression of slow trends [21], commonly adopted to remove ‘scanner drift’; low frequency fluctuations attributed to physiological noise and subject motion [22]; regression of the six motion parameters generated during motion correction [23], which attempts to remove noise that is linearly related to translations and rotations of the head; removal of the global signal [24], which discards the variability in a voxel’s responses that is shared with the fluctuation of the Intersubject MVPD: Empirical comparison of fMRI denoising methods for connectivity analysis average signal in the entire brain; and CompCorr [25], which extracts principal components from the signal in the white matter and cerebrospinal fluid and regresses them out from each voxel
We tested whether intersubject MVPD is able to identify expected patterns of statistical dependence between different brain regions
Summary
Cognitive tasks elicit the activation of multiple brain regions [1, 2]. To understand how these regions function jointly to implement cognition, we need to investigate their connectivity. [3]) and connectivity measures based on functional data [4]) have distinct advantages: the former enable inferences about whether regions are directly connected, the latter have the potential to investigate task-specific changes in the interactions between regions. Perhaps the most popular method to study interactions using functional data is ‘functional connectivity’, an analysis technique based on computing Pearson’s correlation between the average responses in two brain regions over time [4]. Functional connectivity has been used extensively to map brain networks [5] and search for biomarkers for patient populations [6]
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