Denoising the speaking brain: Toward a robust technique for correcting artifact-contaminated fMRI data under severe motion

Abstract

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity.

Methods for cleaning the BOLD fMRI signal

“…Therefore, several procedures have been proposed for assisting automated classification, which mainly differ in the algorithms used for supervised classification (and if necessary feature selection), the number and definitions of the spatial and temporal features used in the classification, as well as the type of fMRI data they are optimized to work with, either task-based, resting state, or both (Beall and Lowe, 2007; Bhaganagarapu et al, 2013; De Martino et al, 2007; Douglas et al, 2011; Formisano et al, 2002; Griffanti et al, 2014; Kochiyama et al, 2005; Liao et al, 2006; Perlbarg et al, 2007; Pruim et al, 2015a; 2015b; Rummel et al, 2013; Salimi-Khorshidi et al, 2014; Sochat et al, 2014; Soldati et al, 2009; Storti et al, 2013; Sui et al, 2009; Thomas et al, 2002; Tohka, et al, 2008; Wang and Li, 2015, Xu et al, 2014). …”

“…during overt speech or swallowing), hindering the ability to differentiate BOLD fMRI from subject motion artefacts despite averaging the response across multiple trials. Task-correlated motion is more problematic in block designs than in event-related designs due to the delay of the haemodynamic response with respect to the rapid effect of motion in the signal (Barch et al, 1999; Birn et al, 1999; Bullmore et al, 1999; Johnstone et al, 2006; Morgan et al, 2007; Oakes et al, 2005; Soltysik and Hyde, 2006; Xu et al, 2014). In resting state fMRI, even small amounts of motion or micromovements within and between scans can significantly confound estimates of functional connectivity between voxel time series.…”

Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process

“…In order to remove susceptibility artifacts generated by motion and physiological noise (blood pulsation, respiration, etc. ), which cannot be removed by the conventional coregistration method, we applied the dual‐mask spatial independent component analysis (sICA) to the motion and slice‐time corrected functional data at the individual subject level [Xu et al, 2014]. The denoised functional data were then normalized into MNI space at a voxel size of 3 × 3 × 3 mm by applying the transforms derived from the structural image normalization and smoothed to a target full‐width‐half‐max of 8 mm.…”

Personal experience with narrated events modulates functional connectivity within visual and motor systems during story comprehension

“…Classification of artifactual and neuronal ICA components was based on their degree of spatial clustering, location of major positively weighted clusters, and neighborhood connectedness between positively and negatively weighted clusters. Using these criteria, the noise components were identified by human experts and their variances were then subtracted from the original dataset [AbdulSabur et al, ; Xu et al, ]. The functional and anatomical images of each participant were coregistered and normalized to the stereotaxic space of the Montreal Neurological Institute (MNI) using SPM8 (http://www.fil.ion.ucl.ac.uk/spm/).…”