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

SUBMITTER: Li Y 

PROVIDER: S-EPMC6759145 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature