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This study analyzed head motion data from over 50,000 participants across six major neuroimaging datasets and found that commonly used motion exclusion criteria remove a substantial, non-random portion of participants. The exclusions disproportionately affect younger and older individuals, those with higher BMI, and people with motion-related clinical conditions, fundamentally altering the demographic composition of research samples. The authors also identified that respiratory pseudo-motion artificially inflates motion estimates in adults, and that strict exclusion thresholds reduce statistical power while changing the apparent relationships between brain measures and behavior.
Why it matters
Current motion-based exclusion practices in brain imaging research may systematically bias scientific findings by removing specific demographic groups, potentially limiting the generalizability of neuroscience research and perpetuating health disparities. The findings suggest that what appears to be a technical quality control decision actually functions as a selection mechanism that reshapes who is represented in our understanding of the human brain.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Large-scale neuroimaging datasets are increasingly used to map relationships between brain structure, function, and behavior across the human lifespan. Routinely, analyses exclude participants who moved too much during imaging. While this decision is framed as quality control, it is increasingly recognized that head motion is not randomly distributed across individuals within a study, and so motion-based exclusion may preferentially remove people with particular characteristics relevant to the scientific goals of the study. Here we survey head motion and how it relates to participant characteristics across six large, publicly available datasets spanning nearly the entire human lifespan, namely the Human Connectome Project (HCP) Young Adult, HCP in Development, HCP in Aging, Adolescent Brain Cognitive DevelopmentSM Study, UK Biobank, and Spatial Topology project. These six datasets comprise more than 50,000 unique participants and 300,000 scans. We further benchmark our findings against motion distributions aggregated by MRIQC across more than 1.5 million scans. Under commonly applied strict exclusion thresholds, large fractions of participants would be removed (exceeding 80% in the UK Biobank task data), and these removals were demographically structured, disproportionately excluding younger and older participants, those with higher BMI, and those with motion-associated clinical conditions. Respiratory pseudo-motion inflated estimates of head motion in adult cohorts, and applying notch filtering to remove respiratory frequencies from these estimates meaningfully reduced exclusion rates. Exclusion also carried downstream consequences. Strict thresholds reduced statistical power, inflated study costs, and altered the apparent predictability of behavioral phenotypes by removing a non-random, behaviorally distinct subgroup. These findings demonstrate that motion exclusion thresholds are not neutral quality-control decisions but structured selection mechanisms that reshape the composition of neuroimaging samples. We recommend that studies report the demographic characteristics of excluded participants, prefer data-driven censoring methods over fixed motion cutoffs, and clarify the target population while considering appropriate weighting techniques.
Source: The State of Motion: Demographic and representational consequences of motion-based exclusion in fMRI