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

TLDR

Since initial reports of motion artifact affecting functional connectivity, many participant‑level confound regression methods have emerged, yet few have been systematically evaluated across diverse outcome measures. The study systematically evaluates 14 participant‑level confound regression methods in 393 youths. The evaluation compares methods across four benchmarks: residual motion–connectivity relationship, distance‑dependent motion effects, network identifiability, and degrees of freedom lost. The results reveal two trade‑offs: global signal regression reduces motion–connectivity correlation but introduces distance‑dependent artifacts, whereas censoring methods eliminate both artifacts at the cost of additional degrees of freedom; less effective denoising also fails to recover modular network structure, highlighting heterogeneous efficacy and the need to align strategies with specific scientific goals.

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.