The impact of image smoothness on intrinsic functional connectivity and head motion confounds

Abstract

We present a novel method for controlling the effects of group differences in motion on functional connectivity studies. Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool that allows for the assessment of whole-brain functional organization across a wide range of clinical populations. However, as highlighted by recent studies, many measures commonly used in rs-fMRI are highly correlated with subject head movement. A source of this problem is that motion itself, and motion correction algorithms, lead to spatial smoothing, which is then variable across the brain and across subjects or groups dependent upon the amount of motion present during scanning. Studies aimed at elucidating differences between populations that have different head-motion characteristics (e.g., patients often move more in the scanner than healthy control subjects) are significantly confounded by these effects. In this work, we propose a solution to this problem, uniform smoothing, which ensures that all subject images in a study have equal effective spatial resolution. We establish that differences in the intrinsic smoothness of images across a group can confound connectivity results and link these differences in smoothness to motion. We demonstrate that eliminating these smoothness differences via our uniform smoothing solution is successful in reducing confounds related to the differences in head motion between subjects.

Keywords: Connectivity; Head movement; Image smoothness; Motion; Resting-state fMRI.

Figure 1

Correlation of connectivity and image smoothness. An image’s intrinsic smoothness is significantly correlated (p<0.05 corrected) with both A) ROI-based and B-C) voxel-based metrics. A) Connectivity to the PCC showed both increased and decreased connectivity as image smoothness increased. B) The voxel based network measure degree showed only increased connectivity as image smoothness increased. C) When the degree maps were normalized to z-scores, areas (such as the PCC) that were not originally correlated with smoothness became negatively correlated with smoothness. Areas of the strongest correlation with smoothness (such as the temporal lobe) in the original map were still significantly correlated with smoothness. Such differences in image smoothness across a sample could lead to artifactual group differences and confound group comparisons.

Figure 2

Correlation of motion and image smoothness. A) Image smoothness showed a significant positive correlation with motion (r=0.28, p<0.004). B) After the data was preprocessed with the uniform smoothing algorithm, image smoothness was no longer significantly correlated with motion (r=0.004, p=0.97).

Figure 3

Uniform smoothing reduces the correlation of the motion timecourse and the BOLD timecourses. A) When the motion timecourse was correlated with the BOLD timecourses for each voxel, motion was significantly correlated (p<0.05 corrected) with the BOLD timecourse for several regions in the brain. B) After uniform smoothing, the magnitude of these correlations were significantly reduced (p<0.05). Figure S3 in Supplemental Material shows the correlation maps for the motion timecourse for each of the preprocessing strategies outlined in Section 2.10.

Figure 4

Correlation between connectivity and motion with and without uniform smoothing. ROI connectivity from the PCC and the voxel-based measure degree showed significant correlation (p<0.05 corrected) with motion. For each, uniform smoothing reduced the correlation with motion. Distributions of these correlations are shown in Figure 8.

Figure 5

Contrast of connectivity results from regular smoothing and uniform smoothing. When the connectivity maps from data smoothed with uniform smoothing were compared with the connectivity maps from data smoothed with standard Gaussian smoothing, connectivity was reduced in several areas for both A) ROI-based and B) voxel-based metrics. Uniform smoothing reduced ROI-based correlations surrounding the PCC ROI and reduced degree throughout the grey matter.

Figure 6

Group average connectivity maps from uniformly smoothed data. Typical patterns of connectivity are observed using uniformly smoothed data. While uniform smoothing reduces connectivity and controls for motion, uniform smoothing does not disrupt the observation of normal brain networks as shown A) for ROI based connectivity to the PCC node and B) for voxel based connectivity using the degree metric.

Figure 7

Changes in correlation between the motion timecourse and the BOLD timecourses for different strategies to minimize motion confounds. (top) All three strategies to minimize motion confounds significantly (p<0.05 corrected) reduced the magnitude of the correlations between the motion timecourse and the BOLD timecourses when compared to standard processing as indicated by the blue regions. Censoring high motion frames increased this correlation in the PCC as indicated by the red/yellow regions. (bottom) In agreement with the voxel wise comparisons, the distributions of these correlations are narrower, as measured by entropy, when compared to standard processing.

Figure 8

Distributions of correlations between connectivity and motion for different strategies to minimize motion confounds. (first row) For PCC connectivity, all strategies produced narrower distributions of correlations with motion as measured by entropy. (second row) For degree, only uniform smoothing produced a narrower distribution of correlations with motion and still remain centered around zero. While the distributions for degree using censoring produced the lowest entropy, these distribution showed an increase in the mean correlation. This increased correlation with motion is also shown in Figure S5. (third row) For normalized degree, uniform smoothing and censoring produced a narrower distribution of correlations with motion. Normalizing helps to center the distributions of correlations.