Related Experiment Video
Updated: Aug 6, 2026
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Comparing Harmonization Approaches for Protocol-Related Variability in Multisite Diffusion MRI Data
Kenny Liou1, Sophia I Thomopoulos1, Julio E Villalon Reina1
1Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Abstract:
Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimer's disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimer's Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (Aβ) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.
Insights
Harmonizing diffusion MRI data across multiple protocols in Alzheimer's Disease Neuroimaging Initiative (ADNI) studies is crucial. Three methods effectively reduced variability while preserving key associations with cognitive decline and disease biomarkers.
Area of Science:
- Neuroimaging
- Radiology
- Neurology
Background:
- Diffusion MRI (dMRI) reveals white matter changes in Alzheimer's Disease (AD).
- Multisite dMRI data present challenges due to varying acquisition protocols.
- The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizes over 10 dMRI protocols.
Purpose of the Study:
- To compare three data harmonization methods for dMRI data.
- To assess the reduction of protocol-related variability in diffusion tensor imaging metrics.
- To evaluate the preservation of associations with cognitive impairment and AD biomarkers.
Main Methods:
- Comparison of three harmonization techniques: mixed-effects models, ComBat-GAM, and eHarmonize.
- Analysis of diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) data.
- Evaluation in 1,086 ADNI3/4 participants with cognitive and PET imaging data.
Main Results:
- All three methods successfully aligned FA and MD distributions across different dMRI protocols.
- Associations between dMRI metrics and cognitive impairment were consistent across harmonization approaches.
- Associations with amyloid-beta and tau PET burden showed more variability.
Conclusions:
- Multiple harmonization strategies effectively address protocol-related variability in multisite dMRI data.
- These methods preserve crucial associations between dMRI metrics and cognitive status in AD.
- Careful consideration of harmonization is essential for robust analysis of pooled dMRI datasets.
