Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity

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 young adults. 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 unmask 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 proposed methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.

Predicting personality from network-based resting-state functional connectivity

“…This procedure was done separately for men and women ( Sample 1 : 5 males, 5 females; Sample 2 : 4 males, 4 females). No subjects were excluded due to outlier motion parameters (DVARS and FD both displaying zero-centered values) (Salimi-Khorshidi et al 2014; Varikuti et al 2016; Ciric et al 2017). For RSFC analyses, the subject-specific time series for each node of each network were computed as the first eigenvariate of the activity time courses of all gray-matter voxels within 6 mm of the respective peak coordinate.…”

Resting state functional connectivity in primary insomnia, generalized anxiety disorder and controls

“…Additionally, Power et al (2014) demonstrated that, in contrast to criticism (Saad et al, 2012), application of GSR actually decreases observed group differences. More recently, GSR has been shown to be especially effective at removing correlations of connectivity with subject motion (Ciric et al, 2017). Given these considerations, we feel GSR is an appropriate processing technique in this study.…”

Graph Theoretic Analysis of Resting State Functional MR Imaging

“…“Preprocessing” techniques to ensure that data meet several assumptions prior to analysis. The most standard steps to preprocessing rsfMRI data include slice timing correction [15], motion correction ([16, 17]), realignment [18], coregistration of anatomical and functional images [19], spatial normalization [20], and smoothing [21]. Smoothing increases signal to noise, normalizing error distributions, and accomodates anatomical and functional variation between subjects.…”