NetBat: A network-driven harmonization method for structural connectivity

As the practice of aggregating multi-site neuroimaging data has become more common, the field of neuroscience has increasingly recognized the importance of harmonization , or the removal of scanner effects from brain imaging data. While many harmonization methods exist, like ComBat and CovBat, few explicitly incorporate the network structure of the brain. Researchers studying structural connectivity are therefore not guaranteed to model the true underlying brain network. This study offers a new harmonization method, called NetBat, which was designed to incorporate network parameters from the weighted stochastic block model (WSBM) as covariates in the popular ComBat harmonization method. NetBat is demonstrated through analysis of eighteen neurotypical individuals each scanned on four MRI scanners. Results suggest that under tested circumstances NetBat provides more accurate overall harmonization and better retention of network structure than competing methods.

Authors

• Gustav R. Sjobeck
• Mahbaneh Eshaghzadeh Torbati
• Davneet S. Minhas
• Charles S. DeCarli
• James D. Wilson
• Dana L. Tudorascu

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