Mitigating head motion artifact in functional connectivity MRI

Abstract

Participant motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, motion artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract motion artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.

FIGURE 1 |. Workflow for motion correction of functional connectivity MRI data.

This processing stream estimates subject movement, minimally pre-processes the functional data, and aligns the data to a high-resolution anatomical reference (co-registration). Next, it builds a confound model using a combination of the global signal, signals from white matter and cerebrospinal fluid, motion estimates, time series expansions, and spike regressors. The confound model is then fit to the data in the confound regression step. A connectome is computed by warping a parcellation into the coordinate space of the time series and calculating functional connectivity between pairs of brain regions. Finally, diagnostic measures are produced to facilitate assessment of model performance and transparent reporting of denoising efficacy. A simplified schematic of key steps is shown at left, while a detailed flowchart of all protocol stages is shown at right. FD, framewise displacement; RPs, realignment parameters; DV, DVARS; WM, white matter; CSF, cerebrospinal fluid; FC, functional connectivity; QC-FC, quality control—functional connectivity correlations.

FIGURE 2 |. Summary of subject-level performance diagnostics and anticipated results

(Steps 28—33). The illustrated results are from a single subject from the Philadelphia Neurodevelopmental Cohort (PNC), processed using a 36-parameter stream that combines 6 realignment parameters, the mean WM and CSF time series, the mean global time series, and derivative and quadratic expansions. The table at left summarises quantitative diagnostics, while the panels at right display visual aids for performance assessment. Left, quantifications of subject movement indicate that this subject remained relatively still, apart from a few brief epochs of high movement. Notably, the GSR-based processing stream abolishes the strong FD-DVARS correlation initially present in the data. Right, a diagnostic visualisation produced by voxts.R. Top right, traces of the subject’s framewise displacement and DVARS (DV). For this analysis, a frame was flagged as a spike if FD exceeded 0.25 or if standardised DVARS exceeded 2. The superthreshold region of the trace is demarcated by a darkened rectangle that covers the top fraction of each trace, allowing identification of flagged frames. In the traces shown, 3 frames exceed the FD threshold and 7 exceed the DVARS threshold. Middle right, a voxelwise carpet plot of the subject’s BOLD activity, computed over the minimally pre-processed image. Time is plotted on the abscissa and is synchronised to the quality traces at the top. Space is plotted on the ordinate, with voxels sorted according to their membership in tissue compartments (bottom right). Within each tissue compartment, time series are sorted from most superficial (bottom) to deepest (top). In this subject, movements are associated with global bands of signal loss (Type 2 artefact in Box 2), which is reflected in functional connectivity as global coupling. Bottom right, after motion correction, the same subject’s BOLD signal no longer exhibits global bands, although some loss of signal variance is visible in the most strongly contaminated frames.

FIGURE 3 |. Summary of group-level performance diagnostics and anticipated results

(Steps 34—36). The illustrated results are from 500 low-motion subjects randomly sampled from the Philadelphia Neurodevelopmental Cohort (PNC). The top row shows performance diagnostics when the data are processed using a 36-parameter stream that combines the mean global time series, 6 realignment parameters, the mean WM and CSF time series, and derivative and quadratic expansions. The bottom row shows analogous results when the same data are processed using a 24-parameter stream that uses only the 6 realignment parameters with derivative and quadratic expansions. The table at left summarises quantitative diagnostics, while the panels at right display visual aids for performance assessment. Results are shown for the 264-node Power parcellation. Top left, effective motion correction leaves only a small percentage of edges that are significantly related to motion, with a weak absolute median QC-FC correlation of approximately 0.04. Bottom left, the 24-parameter model performs poorly by comparison, leaving a marked absolute median QC-FC correlation of 0.226, with nearly all edges exhibiting a significant relationship with motion. Centre right, qcfc.R plots the distribution of QC-FC correlations across the two denoising schemes. In a high-performance processing stream such as the one presented at the top, the distribution is narrow and centred at approximately zero. An ineffective stream, in contrast, has a QC-FC distribution that is broader and positively centred. Far right, a degree of QC-FC distance-dependence is unmasked by the GSR-based processing stream, as is evident in this visualisation produced by featureCorrelation.R. In comparison with the RP-only stream, motion artefact in the GSR-based stream is more strongly related to the Euclidean distance between network nodes. A processing stream that augments GSR with either censoring or signal decomposition techniques will typically exhibit less distance-dependence (see Box 2).