Variations in structural MRI quality significantly impact commonly used measures of brain anatomy

Abstract

Subject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy or indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to more directly measure image quality from T1-weighted images to understand if these measures of image quality: (1) relate to rigorous quality-control checks visually completed by human raters; (2) are associated with sociodemographic variables of interest; (3) influence regional estimates of cortical surface area, cortical thickness, and subcortical volumes from the commonly used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range (N = 388; ages 5–21). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical surface area, cortical thickness, and subcortical volumes from multiple regions across the brain (~ 23.4% of all areas investigated). We believe these results are important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

Event‐related potentials data quality in young children: Standardized measurement error of ERN and Pe

“…We did not find any links between child gender or age and data retention and quality across the time‐window mean amplitude and peak amplitude scores. These age‐related results stand in contrast to a similar investigation of MRI metrics, in which links between age and data quality were reported (Gilmore et al., 2021). We speculate age differences in ERP data quality would be found in developmental samples with wider age ranges, or in longitudinal studies that can track ERP data quality over time.…”

Age-related change in task-evoked amygdala—prefrontal circuitry: a multiverse approach with an accelerated longitudinal cohort aged 4-22 years

“…Finally, our findings on age-related change in amygdala and mPFC function may be biased or confounded by age-related differences in head motion (Ciric et al, 2017), anatomical image quality and alignment (Gilmore et al, 2020; Rorden et al, 2012), signal dropout, and physiological artifacts (Boubela et al, 2015; Fair et al, 2020; Gratton et al, 2020). While our multiverse analyses included preprocessing and group-level modeling specifications designed to minimize some of such potential issues, future work is still needed to optimize discrimination of developmental changes of interest from such potential confounds.…”

Age‐related change in task‐evoked amygdala—prefrontal circuitry: A multiverse approach with an accelerated longitudinal cohort aged 4–22 years

“…Finally, our findings on age‐related change in amygdala and mPFC function may be biased or confounded by age‐related differences in head motion (Ciric et al, 2017), anatomical image quality and alignment (Gilmore, Buser, & Hanson, 2020; Rorden, Bonilha, Fridriksson, Bender, & Karnath, 2012), signal dropout, and physiological artifacts (Boubela et al, 2015; Fair et al, 2020; Gratton et al, 2020). While our multiverse analyses included preprocessing and group‐level modeling specifications designed to minimize some of such potential issues, future work is still needed to optimize discrimination of developmental changes of interest from such potential confounds.…”