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NetBat: A network-driven harmonization method for structural connectivity
Gustav R Sjobeck1, Mahbaneh Eshaghzadeh Torbati1, Davneet S Minhas1
1University of Pittsburgh, Department of Psychiatry, United States of America.
Neuroimage
|June 25, 2025
Summary
Neuroimaging data harmonization is crucial for removing scanner effects. A new method, NetBat, improves accuracy and preserves brain network structure better than existing techniques.
Area of Science:
- Neuroscience
- Medical Imaging
- Network Science
Background:
- Multi-site neuroimaging data aggregation is increasing.
- Harmonization, the removal of scanner effects, is vital for data consistency.
- Existing methods like ComBat and CovBat do not fully account for brain network structure.
Purpose of the Study:
- To introduce NetBat, a novel harmonization method for neuroimaging data.
- To integrate network parameters from the weighted stochastic block model (WSBM) into ComBat.
- To improve the modeling of true underlying brain networks in structural connectivity studies.
Main Methods:
- Developed NetBat by incorporating WSBM network parameters as covariates into the ComBat harmonization method.
- Applied NetBat to analyze structural connectivity data from eighteen neurotypical individuals.
- Each individual underwent MRI scanning on four different scanners to assess harmonization effects.
Main Results:
- NetBat demonstrated more accurate overall harmonization compared to existing methods.
- The proposed method showed superior retention of brain network structure.
- Results indicate NetBat's effectiveness under the tested experimental conditions.
Conclusions:
- NetBat offers an improved approach to neuroimaging data harmonization.
- The method effectively preserves crucial brain network properties.
- NetBat is a promising tool for researchers studying structural connectivity across multiple sites.
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