  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 age-matched, 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.
