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The State of Motion: A survey of motion-related artifacts in fMRI studies

Poster No:

1593

Submission Type:

Abstract Submission

Authors:

Patrick Sadil¹, Briha Ansari¹, Agostina Casamento-Moran¹, Ann Choe¹, Jungin Choi¹, Farzad Farahani², Lukman Ismaila¹, Arunkumar Kannan¹, Cristian Navarro³, Mary Nebel¹, Haris Sair¹, Bonnie Smith¹, Adrian Svingos¹, Martin Lindquist¹

Institutions:

¹Johns Hopkins University, Baltimore, MD, ²Johnson & Johnson, Philadelphia, PA, ³Kennedy Krieger Institute, Baltimore, MD

First Author:

Patrick Sadil, PhD
Johns Hopkins University
Baltimore, MD

Co-Author(s):

Briha Ansari, MD, MPH
Johns Hopkins University
Baltimore, MD

Agostina Casamento-Moran
Johns Hopkins University
Baltimore, MD

Ann Choe, PhD
Johns Hopkins University
Baltimore, MD

Jungin Choi
Johns Hopkins University
Baltimore, MD

Farzad Farahani
Johnson & Johnson
Philadelphia, PA

Lukman Ismaila
Johns Hopkins University
Baltimore, MD

Arunkumar Kannan
Johns Hopkins University
Baltimore, MD

Cristian Navarro
Kennedy Krieger Institute
Baltimore, MD

Mary Nebel, PhD
Johns Hopkins University
Baltimore, MD

Haris Sair
Johns Hopkins University
Baltimore, MD

Bonnie Smith, PhD
Johns Hopkins University
Baltimore, MD

Adrian Svingos, PhD
Johns Hopkins University
Baltimore, MD

Martin Lindquist
Johns Hopkins University
Baltimore, MD

Introduction:

A prominent source of artifacts in functional magnetic resonance imaging (fMRI) is head motion. That motion is problematic is well known, giving rise to both immediate and long-term effects that drastically increase the likelihood of biased results (Friston et al., 1996; Power et al., 2012). Unfortunately, no existing method eliminates motion-induced artifacts, and so researchers often exclude participants who moved substantial amounts from analyses.

Here, we consider two concerns with excluding participants due to motion. First, removing participants decreases sample size, which, if exclusion rates are high enough, can endanger statistical power. Second, movement can be associated with participant characteristics (Bolton et al., 2020; Siegel et al., 2017), and so excluding high movers may decrease the representativeness of study samples (Nebel et al., 2022).

Methods:

We surveyed fMRI data from five publicly available datasets: Human Connectome Project (HCP) Young-Adult (HCPYA), HCP Aging (HCPA), HCP Development (HCPD), Adolescent Brain Cognitive Development (ABCD), and the UK Biobank (UKB). In total, this provided over 62,000 unique participants and over 250,000 scans. For each dataset, we calculated framewise displacement (Power et al., 2012). We then calculated the proportion of participants that would be excluded according to a strict and lenient regime, based on the levels from Parkes et al. (2018): lenient: average over 0.55 mm; strict: either average over 0.25mm, more than 20% of frames over 0.2, or maximum over 5mm. Any characteristic correlated with motion is an aspect that may suffer underrepresentation; we focus on two that are likely correlated with motion: age and BMI. Note that motion was not filtered for respiration and so may be inflated (Fair et al., 2020; Power et al., 2019).

Results:

In all surveyed datasets except for ABCD, the lenient regime excluded less than 3% of participants (Figure 1). In the ABCD dataset, rates of exclusion varied strongly by session. In the baseline session (when children are the youngest), the lenient regime excluded between 10 – 31% of scans. In the strict regime, at least 30% of all scan types were excluded, with one type suffering a 90% exclusion rate (UKB, emotion, both sessions).
To assess how motion interacts with age and BMI, we first checked for correlations with motion. Age and average framewise displacement were correlated: ABCD: _-0.21-0.20_-0.20; HCPA: _0.150.18_0.23; HCPD: _-0.39-0.37_-0.35; HCPYA: _0.090.11_0.12; UKB: _0.220.22_0.23 (ranges span 95% confidence intervals). Likewise, BMI and average framewise displacement were correlated: HCPA: _0.400.42_0.44; HCPD: _0.020.04_0.07; HCPYA: _0.530.54_0.55; UKB: _0.450.45_0.46. We then considered the rates at which people in varied age quartiles and BMI groupings would be excluded under exclusion regimes (Figure 2). In all datasets, the age of the participants excluded under the strict regime were significantly different than the age of the participants who were included (all p<0.01). The BMI for included participants was significantly different in the HPCA, HCPD, and UKB (p<0.01).

Conclusions:

In the surveyed datasets, motion led to substantial rates of exclusion. In the worst cases, motion alone resulted in rates of exclusion that threaten key advantages of consortium-scale datasets: large and diverse samples. Exclusion thresholds always risk arbitrariness, so a result like an 80% excluded should not be taken as implying that 80% of scan are unusable. Rather, it indicates that data loss due to motion is sever enough to warrant grappling with when planning analyses of the public datasets considered here, especially given the induced selection bias.

Modeling and Analysis Methods:

Exploratory Modeling and Artifact Removal ²

Motion Correction and Preprocessing ¹

Keywords:

Data analysis

Design and Analysis

FUNCTIONAL MRI

MRI

Open Data

^1|2Indicates the priority used for review

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Functional MRI

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3.0T

Provide references using APA citation style.

Bolton, T. A. W., Kebets, V., Glerean, E., Zöller, D., Li, J., Yeo, B. T. T., Caballero-Gaudes, C., & Van De Ville, D. (2020). Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI. NeuroImage, 209, 116433. https://doi.org/10.1016/j.neuroimage.2019.116433
Fair, D. A., Miranda-Dominguez, O., Snyder, A. Z., Perrone, A., Earl, E. A., Van, A. N., Koller, J. M., Feczko, E., Tisdall, M. D., van der Kouwe, A., Klein, R. L., Mirro, A. E., Hampton, J. M., Adeyemo, B., Laumann, T. O., Gratton, C., Greene, D. J., Schlaggar, B. L., Hagler, D. J., … Dosenbach, N. U. F. (2020). Correction of respiratory artifacts in MRI head motion estimates. NeuroImage, 208, 116400. https://doi.org/10.1016/j.neuroimage.2019.116400

Friston, K. J., Williams, S., Howard, R., & Frackowiak, R. S. J. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance Imaging, 35(3), 346–355. https://doi.org/10.1002/mrm.1910350312

Nebel, M. B., Lidstone, D. E., Wang, L., Benkeser, D., Mostofsky, S. H., & Risk, B. B. (2022). Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder? NeuroImage, 257, 119296. https://doi.org/10.1016/j.neuroimage.2022.119296

Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage, 171, 415–436. https://doi.org/10.1016/j.neuroimage.2017.12.073

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018

Power, J. D., Lynch, C. J., Silver, B. M., Dubin, M. J., Martin, A., & Jones, R. M. (2019). Distinctions among real and apparent respiratory motions in human fMRI data. NeuroImage, 201, 116041. https://doi.org/10.1016/j.neuroimage.2019.116041

Siegel, J. S., Mitra, A., Laumann, T. O., Seitzman, B. A., Raichle, M., Corbetta, M., & Snyder, A. Z. (2017). Data Quality Influences Observed Links Between Functional Connectivity and Behavior. Cerebral Cortex, 27(9), 4492–4502. https://doi.org/10.1093/cercor/bhw253

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