Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project

Abstract

Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for highmotion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high-and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR.

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

“…Considerable work and debate is now ongoing to determine the optimal ‘denoising’ approaches during data processing (Patriat et al, 2016; Power et al, 2014). The most contentious debate probably concerns the use of global signal regression (GSR) – arguably the most effective motion-denoising technique currently available (Burgess et al, 2016; Power et al, 2015; Yan et al, 2013). Recent data reinforces concerns about artifactual contributions to the global signal in conventional 3T rs-fcMRI acquisitions sensitive to BOLD contrast (Grayson et al, 2016; Power et al, 2016), but also partly substantiates concerns regarding GSR under less conventional, high signal-to-noise scenarios where motion can be physically restricted (Grayson et al, 2016).…”

“…Motion artifacts appear to be tightly related to clinical factors (Fair et al, 2012b) and a whole host of behavioral phenotypes and metrics (Siegel et al, 2016). Some of the techniques currently being used to remove these artifacts are non-optimal (Burgess et al, 2016; Goto et al, 2016). Thus, close examination of many of the developmental MR imaging findings is still warranted.…”

Sensory-biased attention networks in human lateral frontal cortex revealed by intrinsic functional connectivity

“…We used custom MATLAB functions to perform additional preprocessing steps on the MPP+FIX HCP data, including (in order): linear interpolation across high-motion timepoint (> 0.5 mm FD), application of a fourth-order Butterworth temporal bandpass filter to isolate frequencies between 0.009 and 0.08 Hz, temporal denoising and high-motion timepoint censoring via deletion. Evidence exists that physiological artifact still exists in MMP+FIX HCP rs-fMRI data (Burgess et al, 2016), however controversy still exists in the field in regards to the proper methods for addressing temporal noise in rs-fMRI data. To this end, we implemented two separate temporal denoising procedures for the MPP+FIX HCP data to account for physiological artifacts and non-neuronal contributions to the resting state signal: 1) mean ‘grayordinate’ signal regression (MGSR; Burgess et al, 2016) and 2) aCompCor (Behzadi et al, 2007).…”

“…Evidence exists that physiological artifact still exists in MMP+FIX HCP rs-fMRI data (Burgess et al, 2016), however controversy still exists in the field in regards to the proper methods for addressing temporal noise in rs-fMRI data. To this end, we implemented two separate temporal denoising procedures for the MPP+FIX HCP data to account for physiological artifacts and non-neuronal contributions to the resting state signal: 1) mean ‘grayordinate’ signal regression (MGSR; Burgess et al, 2016) and 2) aCompCor (Behzadi et al, 2007). For the MGSR pipeline, the average signal across all cortical vertices and subcortical voxels was calculated and, along with its first temporal derivative, removed from each grayordinate via multiple regression.…”

Multimodal neural correlates of cognitive control in the Human Connectome Project

“…Data were then processed through a highpass filter (cutoff = 0.009 Hz). Data were demeaned and detrended, and no additional spatial smoothing was applied (Burgess, Kandala et al 2016). Runs were then concatenated, and mean timeseries were extracted from a known cortical surface parcellation scheme (Gordon, Laumann et al 2016), with the addition of several parcels from the cerebellum (Culbreth, Kandala et al 2016) and subcortical regions defined by Freesurfer.…”