Optimized fMRI preprocessing pipeline enables robust functional connectivity analysis of mouse brain at laminar level

High-resolution BOLD fMRI has become an essential tool for studying neural circuit and hemodynamic changes at mesoscopic scale. Nevertheless, it is more prone to the poor sensitivity and non-neural signal contamination as the spatial resolution increases. Group-level analysis also imposes new requirements on the subject alignment accuracy. To deal with these challenges, we developed a fMRI preprocessing pipeline featured in random matrix theory-based PCA denoising, one-time image voxel shift correction, and enhanced subject-level alignment. We applied this pipeline to the high-resolution mouse resting state fMRI and achieved high-quality hierarchical connectomes from large brain regions to thin cortical layers.