MRIQC IQM Variability and Artifact Prediction in Multi-Echo fMRI

Assessing BOLD fMRI signal quality is essential for reliable statistical analyses. MRIQC (Esteban et al., 2017) is widely used for single-echo fMRI quality control, extracting image quality metrics (IQMs) per scan and generating visual reports to identify outliers at the group level.For multi-echo (ME) fMRI, MRIQC produces IQMs for each echo, but their variation distributions across echoes and their relationship to artifacts in the final scan remain unclear. This study aims to address these gaps, identify aggregation strategies for IQMs, and predict artifact presence using machine learning.ME-fMRI acquires data at multiple echo times, combining them to improve signal quality (Kundu et al., 2017). This technique has gained traction over the past decade for its ability to better disentangle artifact signals from BOLD signal of interest (DuPre et al., 2021).