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

By Rastko Ciric, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, Mark A. Elliott, Simon B. Eickhoff, Christos Davatzikos and 4 more. NeuroImage, 2017

988 citations recorded by the source corpus.

&NA; 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. HighlightsWe evaluate 14 participant‐level de‐noising pipelines for functional connectivity.Pipeline performance is markedly heterogeneous.GSR minimizes the impact of motion but introduces distance dependence.Censoring reduces motion and improves network identifiability.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fdeefe94a4241b701c5df0d5b2dc3c7d5e8f3071

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Citations in the corpus

Precision functional mapping of individual human brains (2017, positive): “RSFC preprocessing—Additional preprocessing steps to reduce spurious variance unlikely to reflect neuronal activity were executed as recommended in (Ciric et al., 2017; Power et al., 2014).”
Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, background): “…body head motion correction, ventricular and white matter signals, and their ﬁ rst temporal derivatives, quadratic terms, and squares of derivatives (32P) (Ciric et al., 2017; Satterthwaite et al., 2013); and 7) if frame-wise displacement (FD) at any point in time exceeded 0·3 mm, then that time…”
A distributed brain network predicts general intelligence from resting-state human neuroimaging data (2018, background): “Cleaning resting-state fMRI data of the effects of motion remains a very intense topic of research for studies of brain-behavior relationships [83,85,125].”
Comparing spatial null models for brain maps (2021, neutral): “…research using structural MRI ( Bhagwat et al., 2020; Khara-bian Masouleh et al., 2020 ), task fMRI ( Botvinik-Nezer et al., 2020; Carp, 2012 ), resting state fMRI ( Ciric et al., 2017; Parkes et al., 2018 ), and diﬀusion MRI ( Maier-Hein et al., 2017; Oldham et al., 2020; Schilling et al., 2019 ).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “It is also possible to test for residual confounding after preprocessing and compare these residual estimates between groups (Ciric et al., 2017; Parkes, Fulcher, Yucel, & Fornito, 2018).”
Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, methodology): “Resting-state time series data from each participant were processed using the XCP Engine (Ciric et al., 2017), which uses an optimized confound regression procedure to reduce the influence of subject motion (Satterthwaite et al.”
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, methodology): “, 2014, 2013; Satterthwaite et al., 2013) and did not adjust for differences in TR, consistent with common practice in the literature. While the data we analyse here have similar TRs to those studied by Power and colleagues, the spike-regression-based criteria were developed using data acquired at ~”
A practical guide to linking brain-wide gene expression and neuroimaging data (2018, methodology): “The impact of data processing choices on the results of neuroimaging analyses is well documented, with strategies for the correction of motion-related and global signal ﬂ uctuations in functional MRI being a prime example (Power et al., 2015, 2017; Ciric et al., 2017; Parkes et al., 2018).”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, methodology): “, 2014; Power, 2017), we also computed QC-FC correlation metrics widely used in the literature (Satterthewaite et al., 2012; Power et al., 2014; Burgess et al., 2016; Ciric et al., 2017).”