Choice of Processing Pipelines for T1-Weighted Brain MRI Impacts Association and Prediction Analyses

The growing availability of large neuroimaging datasets, such as the UK Biobank, provides new opportunities to improve robustness and reproducibility in brain imaging research. However, little is known about the extent to which MRI processing pipelines influence results. Using 39,655 T1-weighted MRI scans from the UK Biobank, we systematically compared five widely used gray-matter representations derived from three major software packages: FSL (volume-based), CAT12/SPM (volume- and surface-based), and FreeSurfer (cortical and subcortical surface-based). We assessed their impact on morphometricity (trait variance explained by brain features), susceptibility to imaging confounders, false positives, association findings, and prediction accuracy across 29 diverse traits, including lifestyle, metabolic, and disease-related variables. We found that all pipelines were sensitive to imaging confounders such as head motion, brain position, and signal-to-noise ratio, and many produced non-normal voxel or vertex distributions. FSL and FreeSurfer generally yielded higher morphometricity estimates, but each captured partially unique signals, leading to inconsistencies in brain regions identified across methods. Volume-based approaches tended to outperform surface-based ones, detecting more significant clusters, achieving higher replication rates, and producing stronger predictive performance. Small clusters (single voxels or vertices) were less reliable, suggesting caution in their interpretation. Among all methods, FSLVBM emerged as the most consistent all-rounder, maximizing morphometricity, replicability, and predictive accuracy. Our results highlight the strengths and limitations of commonly used processing pipelines, offering benchmarks to guide researchers in method selection. They further suggest that combining multiple pipelines may improve brain-based prediction by leveraging unique, complementary signals, and that careful treatment of imaging confounders is essential for robust large-scale neuroimaging analyses.