Methods to detect, characterize, and remove motion artifact in resting state fMRI

Abstract

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.

Keywords: Artifact; Functional connectivity; MRI; Motion; Movement; Resting state.

Figure 1. Outline of data processing and scrubbing strategies

The column of boxes in the middle depict the general BOLD processing strategy. Part I of the paper only uses the flow of the middle column (no scrubbing). The thick solid gray arrows depict scrubbing as implemented in Part II, in which censoring is only performed after the data are fully processed. The finer dotted gray arrows depict iterative processing as implemented in Part III, in which censoring is incorporated into data processing steps.

Figure 2. Quality control measures

At left and right are data from subjects in the low- and high-motion adult cohorts, respectively. The BOLD data have been demeaned and detrended but have not otherwise undergone functional connectivity processing. Vertical lines denote run borders. At top, the 6 rigid body realignment parameters are shown. Immediately below in red, the framewise displacement (FD) trace is shown, indexing how much the head moves from volume to volume. To give a sense of absolute rotational and translational head displacement, two traces representing the summed absolute values of the translational and rotational realignment parameters are shown. In the third panel the blue DV trace, calculated over a whole brain mask (the mask used to define the global signal, hence the GS subscript) shows the volumetric root mean squared value of the differentiated BOLD timeseries, indexing how much timeseries across the brain change from volume to volume. At bottom, the volumetric mean BOLD signal across the brain (the global signal) is shown with its volumetric standard deviation (SD). FD, DV, and SD measures are elevated during periods of motion. In addition to transient displacements, SD also tracks absolute head displacement (black arrows). RMS movement denotes root mean square realignment estimates. The dotted blue and green lines in the right panel are there simply to provide a reference from which to see elevations in the traces.

Figure 3. QC measures and timeseries in low-motion subjects

The upper traces in each plot are as in Figure 2. The timecourses of voxels in gray matter, white matter, and CSF are shown as intensity plots (at left, the gray bar denotes gray matter, the white bar denotes white matter, and the yellow bar denotes CSF). In the white matter plot, the black bar indicates 10 TRs of data, and the white text indicates the Pearson correlations between the mean gray matter (GM), mean white matter (WM), and mean ventricular (CSF) signals. No motion-associated variance is evident, though there are systematic fluctuations, of varying intensity in various subjects, that presumably reflect neural activity and non-motion-related noise. These data are demeaned and detrended only, as in Figure 2.

Figure 4. QC measures and timeseries in subjects with intermittent movements

These subjects exhibit head movements in which the head departs from and returns to positions near the origin (the dotted absolute displacement traces in the upper panels are not elevated). Motion-related signal changes can be brief or long. They can be decreases, increases, or complex waveforms. They are often but not always similar across voxels. Motion-related variance is variably reflected in white matter or CSF voxels.

Figure 5. QC measures and timeseries in subjects with shifted head position

These subjects exhibit movements that displace the head from the origin over prolonged epochs (the dotted red traces in the top panels). SD traces reflect this absolute displacement. Timeseries reflect this displacement and are often elevated or depressed for long periods by shifted head position.

Figure 6. Pre-regression relationships between mean compartment signals, the global signal, and ROI timecourses

For all 160 subjects, the indicated correlations between the gray matter (GM), the white matter (WM), the ventricular (CSF), and whole-brain (GS) signals were calculated. Additionally, for 264 regions of interest, within-subject averages for the correlations of the 264 timeseries with the global signal were calculated, as well as the mean correlation over all possible pairwise correlations between the 264 ROIs. All timeseries are from demeaned and detrended data, as in Figures 3–5. The values in each subject are plotted as a function of mean FD value and RMS motion. Linear fits including all subjects (gray) or excluding outliers (black) are shown. Signal similarity, generally, is higher in subjects with more movement. Mean FD, generally, is a better predictor of signal similarity than RMS motion. Gray matter signal is highly correlated with the global signal.

Figure 7. Common regressions only partially remove motion-related variance

At top, the data from 3 subjects of Figures 3–5 are re-presented, now with traces (DV, SD, mean signal) representing values derived from gray matter voxels only (instead of whole-brain values). Below the horizontal line, the data after 4 different regression strategies are shown. The top panels represent the 18-parameter regression historically used in the Petersen/Schlaggar lab (12 motion-related, 6 signal-related). The next panels are the same regressors without the global signal and its derivative. The next rows replace the 12 motion-related parameters with 24- and 36-parameter Volterra expansions of realignment estimates. Regardless of the regression strategy, the signal-derived QC measures (DV and SD) indicate artifacts in the post-regression data at periods of motion. Global signal regression visibly removes much of the motion-related signal in addition to non-motion-related signal shared across voxels. Larger numbers of motion-related regressors capture more, but not all, motion-related variance. All scales are identical to those of Figures 3–5. Similar results are seen in Figure S3, where the same analyses are repeated with no tissue-based signals (no GS, WM, or CSF signals).

Figure 8. The temporal limits of motion’s influence on RSFC correlations

This analysis reveals the impact on RSFC correlations of volumes acquired before, during, and after head motion. (A) Illustrations of temporal masks in two subjects. (B) For completely processed data prepared without GSR (top) and with GSR (bottom), the effects of each mask in (A) are shown. Δr is calculated across all subject impacted by a particular mask. The number of subjects impacted by a mask and the mean and standard deviation of remaining data are shown for each analysis (e.g., the N=150 for the 3rd mask means that 150/160 subjects had some volumes with FD > 0.2 mm, and the 10/160 who did not were not included in Δr calculations). TRs prior to motion were examined because frequency filtering can spread artifact backward and forward in time, and TRs subsequent to motion were examined especially due to the prolonged signal changes seen in Figure 4.

Figure 9. Motion scrubbing selectively decreases group differences

These plots show, for analyses with GSR (left) and without GSR (right), the distance-dependent changes in correlation produced by FD-targeted scrubbing in various cohorts at various thresholds (a lenient threshold of FD > 0.5 mm and a strict threshold of FD > 0.2 mm). N indicates the number of subjects in the analysis, and the numbers below indicate the mean and standard deviation of the percentage of data remaining after scrubbing. At bottom, the number of significant differences between low-motion and high-motion adult cohorts are shown, out of ~35,000 pairwise correlations, as determined by a two-sample t-test. The error bars on the random bars are the standard deviations across 30 repetitions of random censoring. Comparisons of all adult cohorts at other statistical thresholds yield similar patterns and are shown in Figure S5. In these analyses, unlike other Figures, mean Δr is calculated across all subjects in a cohort, regardless of whether any volumes were censored, to illustrate the types of ‘bottom line’ changes in RSFC that would actually be seen in cohorts upon scrubbing and entire dataset.

Figure 10. An illustration of the evolution of signal-based QC measures through functional connectivity processing

At left, for a single subject, FD traces are shown at top, and DV and SD traces are shown at different steps of functional connectivity processing. DV and SD traces evolve throughout processing. For DV and SD, the horizontal lines represent, across all subjects, a threshold 2 standard deviations above the median value (the number beside the plot). At right, in Cohort 1, the across-subject Δr (N=40) produced by censoring volumes above the thresholds displayed at left. QC values from any stage of processing produce temporal masks with similar effects. These results are obtained by censoring fully processed timeseries; similar effects are seen when timeseries from any stage of processing are censored (data not shown).

Figure 11

A) Scrubbing of volumes that begin with high outlying DV values before functional connectivity processing but which then exhibit DV values within 1 s.d. of the median DV value following nuisance regression. The timeseries are post-regression, pre-frequency-filtering (other stages showing similar effects are shown in Figure S9). B) Top, for a single subject, the within-subject changes in mean short-distance correlation seen in a 50-volume sliding window are shown as a function of the highest FD value found within each window. The black points establish random expectations, and are produced by random orderings of the data. Bottom, a heat map showing the across-subject distribution of empirical ranks within binned QC ranges. Rank bins are 0–100% in 5% bins. With 20 rank bins, 5% of the data should fall in each rank bin by chance. The black sigmoidal trace is the cumulative distribution of datapoints across subjects.

Figure 12. Scrubbing and reprocessing reduce QC-RSFC correlations

For all 120 adults, mean FD was correlated across subjects with each pairwise correlation under several processing regimes. The histograms plot the observed and random QC-RSFC relationships observed under each processing stream.

Figure 13. Adult group differences under different processing strategies

This figure shows the number of group differences expected by chance (black) and the number of observed differences seen between the adult cohorts under different processing strategies. The number of significant differences is defined by p > 0.00005 in 2-sample t-test, as in Figure 9. Identical analyses with stricter and more lenient statistical thresholds (those of Figure S5) yield similar results and are shown in Figure S10. Permutation tests (10,000 fold) among the 120 adults established null expectations and the significance level of the observed group differences. The numbers above the red bars are the number of differences, which were much greater than the other group differences.

Figure 14. Some limitations of group-level regression

A) A diagram of how across-group and within-group regression may and may not correct artifactual increases in correlations. In these simulations, a ‘true’ value is specified for each cohort, as is an FD distribution (low-motion and high-motion). ‘Observed values‘ are computed by adding a constant beta multiplied by the subject’s FD value to the ‘true’ value. ‘Across-group corrected‘ values reflect residuals after a linear fit is made across both cohorts, and ‘within-group corrected‘ values reflect residuals (intercepts retained) after a linear fit is made within each cohort. The ‘within-group’ correction works because the rise in r per unit FD is linear and identical in both groups. B) For 3 randomly selected pairwise relationships, the RSFC correlations (without GSR) of 120 adult subjects are plotted as a function of each subject’s mean FD. Lines show linear QC-RSFC fits on different subsets of the data, including all subjects (black), the actual FD-binned cohorts of the paper (red), and randomly-formed 40-subject cohorts that have indistinguishable mean FD distributions. C) The beta values at 100 randomly selected pairwise correlations are shown below. D) Across all pairwise correlations, the distribution of beta values in the different cohorts of (B, C). The betas in the low-motion cohort are higher and span a broader range of values than the betas found in the other cohorts. These data were prepared using regressors [WM WM′ CSF CSF′ R R² R_t-1 R_t-1²].

Figure 15. Discussion points

A) The 264–264 average correlation matrix in the adult cohorts under different processing streams. B) Histograms of the correlation values found without GSR in the adult cohorts. C) Red vectors show where negative correlations are located in (B). D) Plots of RSFC correlation values under different processing strategies. Points under the line have higher values under the processing indicated on the X axis. E) Comparison of optimal within-subject processing vs. optimal processing without GSR in high-motion adults. F) Comparison of processing with and without GSR in low-motion adults.