Neuroimaging methods are increasingly common, but with these advancements, there has been a greater understanding of the potential confounds and limitations of these research techniques. One of the most common limitations of neuroimaging research is that of motion-related artifacts. This type of noise is caused by participant movement during a neuroimaging session and may impact assessment of brain structure and function1–4. For those interested in neurodevelopment and mental health, such noise and bias may be particularly important to address. While head motion varies considerably among individuals, children typically move more than adults and patient groups move on average more than controls5,6.
Multiple resting state fMRI studies have highlighted the importance of this issue, as incredibly small differences in motion have been shown to yield significant differences in estimates of functional connectivity among healthy samples1,3. In fact, head movements within fractions of a millimeter have been shown to significantly bias correlations between BOLD-activation time series’ in a distant dependent manner, leading to spurious estimates of connectivity within functional networks3,7. Further, recent work has shown that head motion is consistent within individual subjects from one scanning session to the next, raising the potential for motion to confound the exploration of individual differences within the same population8. Particularly challenging, these differences persist even after extensive motion correction procedures9,10. This has thus motivated a methodological sub-field focused on effective ways to reduce motion-related noise in resting-state and other forms of functional MRI.
While a great deal of progress has been made in quantifying and addressing the impact of head-motion in functional analyses, less attention has been given to structural MRI. It is, however, clear that head motion has been shown to compromise derived measures of volume and thickness in regions of cortical gray matter11–13. Such effects remain after automated correction, suggesting that in-scanner motion induces spurious effects that do not reflect a processing failure in software; rather, they reflect systematic bias (e.g., such as motion-induced blurring) and this may appear similar to gray matter atrophy12. These may be particularly important issues to examine in youth and/or clinical populations.
While the impact of movement on structural MRI is clear, methods of quantifying and addressing motion-related noise in structural MRI have been limited. With particularly noisy structural data, researchers traditionally “flag” problematic scans and remove these subjects from further analyses. This process involves raters visually assessing each T1-weighted structural image. A limitation of this strategy is that many phenotypes of interest are inherently more prone to head motion (e.g., children under 9; individuals with clinical diagnoses11,13). Also, human rating systems are relatively impractical for large scale datasets. A further challenge is that visual inspection by human raters is relatively subjective. Numerous studies have showcased this, with moderately concerning inter- and intra- related variability among human-rating systems14. Further, even for structural scans that pass “visual inspection”, there may still be important variations in data quality which impact morphometric estimates. Put another way, some scans may be “just above” threshold for raters, while other volumes may be of utmost quality; both types of scans, however, would be simply considered “usable”11.
Thinking holistically, these multiple problems are in part due to the limited information about noise typically available for structural MRI scans. Structural MRI involves the acquisition of only one, higher resolution volume. To date, this has prohibited rich assessments of noise and subject movement in contrast to fMRI. Functional MRI involves the acquisition of dozens, often hundreds, of lower resolution brain volumes; this allows for the calculation of frame-by-frame changes in a volume’s position, and a clear metric of subject movement during fMRI scanning acquisitions. The ease in collection of this sort of data has led some to advocate for the use of fMRI-derived motion parameters, such as mean Framewise Displacement (FD), to identify structural brain scans that contain motion-related bias. Recent work has showed that by additionally removing FD outliers from a sample of visually inspected T1-weighted images, the effect sizes of age and gray matter thickness were attenuated across a majority of the cortex15. It is, therefore, possible that some past results of associations between participant variables and brain morphometry may be inaccurate, likely particularly inflated in “motion-prone” populations. Additional work would be necessary to clarify precisely how motion-related bias and noise in T1w images varies and overlaps across distinct study populations.
While past structural MRI studies have suffered from the limitations noted above, advancements of novel informatic tools may overcome these issues. Quality assessment tools have been recently introduced that provide easy-to-implement, automated, quantitative measures of structural neuroimaging volumes. For example, the MRI Quality Control tool (MRIQC) has recently been introduced and can speak to different quality attributes of structural (and other MRI) images16. Similarly, the Computational Anatomy Toolbox for SPM (CAT12) assesses multiple image quality metrics and provides an aggregate “grade” for a given structural MRI scan17. Thinking about past research, it is unclear if structural MRI quality is related to commonly derived structural measures (e.g., cortical thickness; regional subcortical volumes). Thoughtful work by Rosen and colleagues18 began to investigate this idea. These researchers found that metrics from Freesurfer, specifically Euler number, were consistently correlated with human raters’ assessments of image quality. Furthermore, Euler number, a summary statistic of the topological complexity of a reconstructed brain surface, was significantly related to cortical thickness.
While important, one of Rosen and colleagues’ major results could be described as “circular” in nature– a measure of Freesurfer re-construction (Euler number) is related to measures output by Freesurfer (cortical thickness)18. In theory, inaccuracy or variability of Freesurfer re-construction could be due to MR quality and/or algorithmic issues. The use of an independent measure of quality in relation to Freesurfer outputs would provide stronger evidence of the potential impact of MRI quality on morphometric measures. In addition, Rosen and colleagues did not investigate if Euler number, their measure of MR quality, was related to subcortical (e.g., amygdala) volumes. Given the major interest from cognitive and affective neuroscientists in these areas19,20, it will be important to know if MRI quality impacts volumetric variations in these structures. Accounting for such variations may be important in reducing potential spurious associations and increasing the replicability of effects.
To these ends, we investigated three key questions: 1) if an integrated measure of image quality, output by the CAT12 toolbox uniquely related to visual rater judgement (retain/exclude) of structural MRI images; 2) if variations in image quality related to sociodemographic and psychosocial variables (e.g., age; sex; clinical diagnosis); 3) if CAT12 image quality was associated with differences in commonly-used structural measures derived from Freesurfer (both cortical thickness and subcortical volume).