Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites

Hanliang Xu; Nancy R. Newlin; Michael E. Kim; Chenyu Gao; Praitayini Kanakaraj; Aravind R. Krishnan; Lucas W. Remedios; Nazirah Mohd Khairi; Kimberly Pechman; Derek Archer; Timothy J. Hohman; Angela L. Jefferson; Ivana Isgum; Yuankai Huo; Daniel Moyer; Kurt G. Schilling; Bennett A. Landman

doi:10.1117/12.3005563

2 April 2024 Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites

Hanliang Xu, Nancy R. Newlin, Michael E. Kim, Chenyu Gao, Praitayini Kanakaraj, Aravind R. Krishnan, Lucas W. Remedios, Nazirah Mohd Khairi, Kimberly Pechman, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Ivana Isgum, Yuankai Huo, Daniel Moyer, Kurt G. Schilling, Bennett A. Landman

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129261X (2024) https://doi.org/10.1117/12.3005563
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 agematched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods’ effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Hanliang Xu, Nancy R. Newlin, Michael E. Kim, Chenyu Gao, Praitayini Kanakaraj, Aravind R. Krishnan, Lucas W. Remedios, Nazirah Mohd Khairi, Kimberly Pechman, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Ivana Isgum, Yuankai Huo, Daniel Moyer, Kurt G. Schilling, and Bennett A. Landman "Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261X (2 April 2024); https://doi.org/10.1117/12.3005563

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