Harmonization mitigates diffusion MRI scanner effects in infancy: insights from the HEALthy Brain and Childhood Development (HBCD) study

TL;DR

This study demonstrates that applying ComBat-GAM harmonization effectively reduces scanner-related variance in diffusion MRI data from the HBCD study, ensuring more accurate analysis of brain development in infants.

Contribution

The paper systematically reports HBCD-specific scanner effects and shows that ComBat-GAM harmonization eliminates these effects across multiple scanner models.

Findings

Post-harmonization, no significant scanner differences remain.

Harmonization reduces Cohen's f effect sizes across metrics.

Highlights the necessity of harmonization in large-scale neuroimaging studies.

Abstract

The HEALthy Brain and Childhood Development (HBCD) Study is an ongoing longitudinal initiative to understand population-level brain maturation; however, large-scale studies must overcome site-related variance and preserve biologically relevant signal. In addition to diffusion-weighted magnetic resonance imaging images, the HBCD dataset offers analysis-ready derivatives for scientists to conduct their analysis, including scalar diffusion tensor (DTI) metrics in a predetermined set of bundles. The purpose of this study is to characterize HBCD-specific site effects in diffusion MRI data, which have not been systematically reported. In this work, we investigate the sensitivity of HBCD bundle metrics to scanner model-related variance and address these variations with ComBat-GAM harmonization within the current HBCD data release 1.1 across six scanner models. Following ComBat-GAM, we observe zero statistically significant differences between the distributions from any scanner model following FDR correction and reduce Cohen's f effect sizes across all metrics. Our work underscores the importance of rigorous harmonization efforts in large-scale studies, and we encourage future investigations of HBCD data to control for these effects.