Optimizing preprocessing and analysis pipelines for single‐subject fMRI. I. Standard temporal motion and physiological noise correction methods

Abstract

Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747-771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89-95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or "pipeline") in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was CIHR Author Manuscript CIHR Author Manuscript CIHR Author Manuscriptdemonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods.

Evaluation and calibration of functional network modeling methods based on known anatomical connections

“…There are processing approaches to correct these unavoidable confounds such as motion correction and a field gradient which attempt to align scans and adjust for field changes (Ing and heartbeat and rate of breathing to extract those frequency components from the measured signal (Glover et al, 2000;Birn et al, 2006;Lund et al, 2006), and removing the signal which is common to all regions, called the global signal (Zarahn et al, 1997;Fox et al, 2005;Carbonell et al, 2011). While motion correction is now widely accepted as a necessary step in fMRI studies (Churchill et al, 2012), the consensus on the removal of the global signal has not yet been met. While it seems probable that such a signal would not contain information interesting in studies which contrast the differences in activations of brain areas Carbonell et al, 2011;, some believe that it may contain useful information in potentially contained networks (Golland et al, 2007), or that its removal will alter the relationship structures across regions in the brain (Aguirre et al, 1998;Desjardins et al, 2001;Gavrilescu et al, 2002;Laurienti, 2004;.…”

“…Fox et al, 2005;Carbonell et al, 2011). While motion correction is now widely accepted as a necessary step in fMRI studies (Churchill et al, 2012), the consensus on the removal of the global signal has not yet been met. While it seems probable that such a signal would not contain information interesting in studies which contrast the differences in activations of brain areas Carbonell et al, 2011;, some believe that it may contain useful information in potentially contained 26 networks (Golland et al, 2007), or that its removal will alter the relationship structures across regions in the brain (Aguirre et al, 1998;Desjardins et al, 2001;Gavrilescu et al, 2002;Laurienti, 2004;.…”

Reprint of: Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development

“…Block designs are particularly susceptible to motion effects as head motion associated with potential reactions to stimuli or button presses to variables of interest can correlate highly with the sustained HRF within a block (Johnstone et al, 2006). If this correlation is low then the signal will be increased by this process and if the correlation is high then it will be decreased (Bright and Murphy, 2015;Bullmore et al, 1999;Churchill et al, 2012;Johnstone et al, 2006;Ollinger et al, 2009). Event-related designs can be less problematic (Birn et al, 1999); event-related paradigms tend to involve brief response to randomly temporally separated events of interest and the responserelated motion will have a different temporal shape than the smooth HRF associated with the BOLD signal (Johnstone et al, 2006).…”

The Stability of Behavioral PLS Results in Ill-Posed Neuroimaging Problems

“…Control blocks involved touching a fixation cross presented at random intervals of 1-3 s. The resulting 4D f MRI time series were preprocessed using standard tools from the AFNI package, including rigid-body correction of head motion (3dvolreg), physiological noise correction with RETROICOR (3dretroicor), temporal detrending using Legendre polynomials and regressing out estimated rigid-body motion parameters (3dDetrend, see [8] for an overview of preprocessing choices in f MRI). For the majority of results (see Sections 2.2 and 2.3), we preprocessed the data using a framework that optimizes the specific processing steps independently for each subject, as described in [18,19], within the split-half NPAIRS resampling framework [6]. In Section 2.4, we provide more details of pipeline optimization, and demonstrate the importance of optimizing preprocessing steps on an individual subject basis in the PLS framework.…”

“…However, taking our cue from the literature on split-half discriminant analysis in f MRI (see, e.g., [7,10,18,19,22]), we can regularize and de-noise the data space in which the analysis is performed, by first applying PCA to X, and then running a PLS analysis on a reduced PCA subspace.…”