Abstract

Motivation: The Human Connectome Project (HCP) is a multi-site neuroimaging initiative that studies brain connections across the lifespan and in diseases. Scanner variability, especially in diffusion MRI (dMRI) data, can introduce bias and prevent reliable pooling of data. Goal(s): We present our harmonization efforts on the dMRI data from 11 HCP datasets using a well-validated harmonization algorithm based on rotation-invariant spherical harmonics. Approach: Using several diffusion measures, we demonstrate that harmonization removes significant statistical differences between datasets. Results: Harmonized HCP dMRI data will be shared in the NIMH Data Archive and facilitate large-scale analysis and potentially enhance our understanding of neurological and psychiatric disorders. Impact: Harmonizing diffusion MRI data from 11 Human Connectome Project scanners enables more reliable cross-site brain connectivity analyses. Leveraging large-scale harmonized diffusion MRI data can enhance statistical power, paving the way for advanced neurological and psychiatric insights within the HCP study.