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. 2017 Jul 1;154:174-187.
doi: 10.1016/j.neuroimage.2017.03.020. Epub 2017 Mar 14.

Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity

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Free PMC article

Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity

Rastko Ciric et al. Neuroimage. .
Free PMC article

Abstract

Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.

Keywords: Artifact; Confound; Functional connectivity; Motion; Noise; fMRI.

Figures

Figure 1
Figure 1. Schematic of the 14 de-noising models evaluated in the present study
For each of the 14 models indexed at left, the table details what processing procedures and confound regressors were included in the model. De-noising models were selected from the functional connectivity literature and represented a range of strategies.
Figure 2
Figure 2. Number of edges significantly related to motion after de-noising
Successful de-noising strategies reduced the relationship between connectivity and motion. The number of edges (network connections) for which this relationship persists provides evidence of a pipeline's efficacy. A, The percentage of edges significantly related to motion in a 264-node network defined by Power et al. (2011). Fewer significant edges is indicative of better performance. B, The percentage of edges significantly related to motion in a second, 333-node network defined by Gordon et al. (2016). C, Renderings of significant edges with QC-FC correlations of at least 0.2 for each de-noising strategy, ranked according to efficacy. Strategies that include regression of the mean global signal are framed in blue and consistently ranked as the best performers.
Figure 3
Figure 3. Residual QC-FC correlations after de-noising
The absolute median QC-FC correlation is another measure of the relationship between connectivity and motion. A, The absolute median correlation between functional connectivity and motion in a 264-node network defined by Power et al. (2011). A lower absolute median correlation indicates better performance. B, The absolute median correlation between functional connectivity and motion in a second, 333-node network defined by Gordon et al. (2016). C, Distributions of all edgewise QC-FC correlations after each de-noising strategy, ranked according to efficacy. Results largely recapitulated those reported in Figure 2, with GSR-based approaches (blue frame) collectively exhibiting the best performance. Whereas approaches that included more regressors generally yielded a narrower distribution, those approaches that included GSR tended to shift the distribution's center toward 0.
Figure 4
Figure 4. Distance-dependence of motion artifact after de-noising
The magnitude of motion artifact varies with the Euclidean distance separating a pair of nodes, with closer nodes generally exhibiting greater impact of motion on connectivity. A, The residual distance-dependence of motion artifact in a 264-node network defined by Power et al. (2011) following confound regression. B, The residual distance-dependence of motion artifact in a second, 333-node network defined by Gordon et al. (2016). C, Density plots indicating the relationship between the Euclidean distance separating each pair of nodes (x-axis) and the QC-FC correlation of the edge connecting those nodes (y-axis). The overall trend lines for each de-noising strategy, from which distance-dependence is computed, are indicated in red. For each plot, the ordinate is rescaled to the data; thus, the ordinate does not reflect the width of the distribution of QC-FC correlations. (The same data is plotted to a common ordinate in Supplemental Figure 1.) The best performing models either excised high-motion volumes (36-parameter + scrubbing) or used more localized regressors (ICA-AROMA and wmLocal). In general, approaches that made use of GSR without censoring resulted in substantial distance-dependence. This effect was driven by differential efficacy of de-noising, with effective de-noising for long range connections but not short-range connections.
Figure 5
Figure 5. Identifiability of network structure after de-noising
Although de-noising approaches remove motion artifact from BOLD time series, it is possible that they also remove signal of interest. We quantified the retention of signal of interest as the modularity quality of the de-noised connectome. A, The modularity quality in a 264-node network defined by Power et al. (2011) following confound regression. B, The modularity quality in a second, 333-node network defined by Gordon et al. (2016). ICA-, GSR-, and tissue class-based models performed relatively well, while models that included realignment parameters alone did not remove enough noise to accurately identify network structure.
Figure 6
Figure 6. Correlation between subject motion and modularity quality
Motion affects network modularity to varying degrees for different de-noising approaches. We quantified the retention of signal of interest as the modularity quality of the de-noised connectome. A, The correlation between subject motion and modularity quality in a 264-node network defined by Power et al. (2011) following confound regression. B, The correlation between subject motion and modularity quality in a second, 333-node network defined by Gordon et al. (2016). In general, GSR- and ICA-based methods most effectively decoupled network structure from artifact.
Figure 7
Figure 7. Estimated loss of temporal degrees of freedom for each pipeline evaluated
Bars indicate mean number of additional regressors per confound model; error bars indicate standard deviation for models where the number of confound regressors varies by subject. High-parameter models and framewise censoring performed well overall on other benchmarks, but were also costliest in terms of temporal degrees of freedom. Despite this cost, augmenting a high-parameter model with censoring improved signal detection (see Figure 5), suggesting that the lost degrees of freedom corresponded largely to noise. Because the 36P+despike model censors data in a spatially adaptive manner, the DOF loss in this case varied by voxel, and is not displayed.

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