title: GLMdenoise: a fast, automated technique for denoising task-based fMRI data.
abstract: In task-based functional magnetic resonance imaging (fMRI), researchers seek to measure fMRI signals related to a given task or condition. In many circumstances, measuring this signal of interest is limited by noise. In this study, we present GLMdenoise, a technique that improves signal-to-noise ratio (SNR) by entering noise regressors into a general linear model (GLM) analysis of fMRI data. The noise regressors are derived by conducting an initial model fit to determine voxels unrelated to the experimental paradigm, performing principal components analysis (PCA) on the time-series of these voxels, and using cross-validation to select the optimal number of principal components to use as noise regressors. Due to the use of data resampling, GLMdenoise requires and is best suited for datasets involving multiple runs (where conditions repeat across runs). We show that GLMdenoise consistently improves cross-validation accuracy of GLM estimates on a variety of event-related experimental datasets and is accompanied by substantial gains in SNR. To promote practical application of methods, we provide MATLAB code implementing GLMdenoise. Furthermore, to help compare GLMdenoise to other denoising methods, we present the Denoise Benchmark (DNB), a public database and architecture for evaluating denoising methods. The DNB consists of the datasets described in this paper, a code framework that enables automatic evaluation of a denoising method, and implementations of several denoising methods, including GLMdenoise, the use of motion parameters as noise regressors, ICA-based denoising, and RETROICOR/RVHRCOR. Using the DNB, we find that GLMdenoise performs best out of all of the denoising methods we tested.
authors.affiliation: Department of Psychology, Washington University in St. Louis St. Louis, MO, USA.
authors.forename: Kendrick N
authors.initials: KN
authors.lastname: Kay
authors.affiliation: Department of Psychology, Stanford University Stanford, CA, USA.
authors.forename: Ariel
authors.initials: A
authors.lastname: Rokem
authors.affiliation: Department of Psychology, New York University New York, NY, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Winawer
authors.affiliation: Center for Cognitive and Neurobiological Imaging, Stanford University Stanford, CA, USA.
authors.forename: Robert F
authors.initials: RF
authors.lastname: Dougherty
authors.affiliation: Department of Psychology, Stanford University Stanford, CA, USA.
authors.forename: Brian A
authors.initials: BA
authors.lastname: Wandell
keywords: BOLD fMRI; ICA; RETROICOR; correlated noise; cross-validation; general linear model; physiological noise; signal-to-noise ratio