Signal-to-noise ratio estimates predict head motion presence in T1-weighted MRI

MRIQC (Esteban et al. 2017) is a tool to help researchers perform quality control (QC) on their structural and functional MRI data. Not only does MRIQC generate visual reports for reliable, manual assessment but it also automatically extracts a set of image quality metrics (IQMs). However, these IQMs are hard to interpret, and many related questions remain open, such as which IQMs are more important. In this project, which emerged as a BrainHack Geneva 2022 initiative, we show that head motion during the acquisition of whole-brain T1-weighted (T1w) MRI of healthy volunteers can be predicted based on the IQMs using supervised machine learning. To do so, we employ the open MR-ART (movement-related artifacts; Nárai et al. 2022) dataset, which includes T1w images acquired under three different motion conditions. We show that signal-to-noise ratio (SNR) derived metrics are the most important features to predict motion presence.