Beyond the General Linear Model: Linear Mixed-Effects Modeling of fMRI Data from the Human Connectome Project

Abstract We present a validated, containerised pipeline for Linear Mixed-Effects (LME) analysis of task fMRI, built for reproducibility: the fitting backend must be declared explicitly, outputs are versioned, and every step records a manifest of software, hardware, and parameters. The implementation is cross-validated against the R reference standard (mean $$\varvec{|\Delta \text {ICC}| = 2.2\times 10^{-6}}$$ across 21 regions; significance concordance 21/21) and benchmarked at $$\varvec{19\times }$$ the per-voxel cost of the GLM, completing a whole-brain analysis in under six hours with 100% convergence. In a case study on emotion task data from 142 HCP subjects (283 runs), voxelwise agreement with the conventional General Linear Model (GLM) is high ( $$\varvec{r = 0.94}$$ ), with LME more conservative under FDR correction (74,004 vs. 108,627 significant voxels at $$\varvec{q < 0.05}$$ ; Dice $$\varvec{= 0.77}$$ ). An intermediate LME omitting the run fixed effect is almost indistinguishable from the GLM (Dice $$\varvec{= 0.99}$$ ), locating the sensitivity difference in the run covariate rather than in hierarchical variance decomposition. Whole-brain ICC mapping shows heterogeneous within-subject variability (mean ICC $$\varvec{= 0.199}$$ ), and run effects are significant in 13/21 ROIs, surviving adjustment for head motion. This decomposition strategy offers a reusable diagnostic for sensitivity differences between statistical approaches.