A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

Yan, Chao-Gan; Cheung, Brian; Kelly, Clare; Colcombe, Stan; Craddock, R. Cameron; Di Martino, Adriana; Li, Qingyang; Zuo, Xi-Nian; Castellanos, F. Xavier; Milham, Michael P.

doi:10.1016/j.neuroimage.2013.03.004

Cited by 1,461Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

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Yan, Chao-Gan, Cheung, Brian, Kelly, Clare, Colcombe, Stan, Craddock, R. Cameron, Di Martino, Adriana, Li, Qingyang, Zuo, Xi-Nian +2 more·2013·NeuroImage

DOI 10.1016/j.neuroimage.2013.03.004PMC PMC3896129

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Abstract

Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that "micro" head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD>0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics - particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.

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Global signal regression is arguably the most effective motion-denoising technique currently available.
“The most contentious debate probably concerns the use of global signal regression (GSR) – arguably the most effective motion-denoising technique currently available (Burgess et al., 2016; Power et al., 2015; Yan et al., 2013).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Yan et al., 2013
We used GSR because it offers a number of benefits such as improving the correspondence between resting-state correlations and anatomy and motion correction at the time series level.
“We used GSR because it offers a number of benefits such as improving the correspondence between resting-state correlations and anatomy [54] and motion correction at the time series level [55], [56].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [56]

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Authors: Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P.
Affiliations: Nathan Kline Institute for Psychiatric Research (US)
Child Mind Institute (US)
NYU Langone Health (US)
Chinese Academy of Sciences (CN)
Corresponding author: Milham, Michael P.
ORCID: Yan, Chao-Gan
Cheung, Brian
Kelly, Clare
Colcombe, Stan
Craddock, R. Cameron
Li, Qingyang
Zuo, Xi-Nian
Castellanos, F. Xavier
Milham, Michael P.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2013-08
Volume: 76
Pages: 183-201
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2013.03.004
PMC: PMC3896129
OpenAlex: W1980527160
Cited by: 1,461
References: 86
Citations by year: 2026: 60 · 2025: 70 · 2024: 82 · 2023: 95 · 2022: 127 · 2021: 110 · 2020: 146 · 2019: 148 · 2018: 132 · 2017: 131 · 2016: 121 · 2015: 119 · 2014: 77 · 2013: 41
FWCI: 65.19
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
Funders: National Institute of Mental Health (R01MH094639, R01MH083246, R01MH081218, K23MH087770)
Stavros Niarchos Foundation
Leon Levy Foundation
Ryan Licht Sang Bipolar Foundation
Brain and Behavior Research Foundation
Autism Speaks
National Natural Science Foundation of China (81171409, 81220108014)
Startup Foundation for Distinguished Research Professor of the Institute of Psychology (Y0CX492S03)
Chinese Academy of Sciences (Y2CX112006, KSZD-EW-TZ-002)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords: head movement, resting-state fMRI, functional connectome, motion correction, global signal regression, interindividual variability, test-retest reliability, amplitude of low-frequency fluctuation, primary motor cortex, prefrontal cortex, children
MeSH terms: Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Motion, Neural Pathways, Rest, Artifacts, Head Movements, Connectome

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A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

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