The impact of quality control on cortical morphometry comparisons in autism

Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons. However, despite this, limited work has systematically evaluated the impact of image and image-processing quality on these measures, or compared different quality control (QC) methods and metrics. The growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. Two of the most important aspects of MRI quality are motion, which is known to have a substantial impact on cortical measures in particular, and the accuracy of processed outputs, which have been shown to impact neurodevelopmental trajectories. Here, we present a tool, FSQC, that enables quick and efficient yet thorough assessment of both of these aspects in outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, and two other manual ratings of raw image quality. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the ABIDE dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons.