Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging

Abstract

Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel-and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that featurewise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.

Learning site-invariant features of connectomes to harmonize complex network measures

“…Alzheimer's Disease Neuroimaging Initiative (ADNI) 16 and National Alzheimer's Coordinating Center (NACC) 17 incorporates data from multiple scanner vendors and protocols, Open Access Series of Imaging Studies (OASIS3) 18 includes multiple protocols, and Baltimore Longitudinal Study of Aging (BLSA) 19 has data from distinct scanning locations. These site specifications may introduce significant confounding differences in DWI and downstream connectomic analysis [20][21][22][23] . Vollmar et.…”

“…Schilling et al showed that fiber bundle shape and microstructure analysis was affected by scanner manufacturer, acquisition protocol, diffusion sampling scheme, diffusion sensitization and overall bundle processing workflow 22 . Joint datasets of complex network measures in Newlin et al 23 and Onicas et al 25 show that modularity, global efficiency, clustering coefficient, density, characteristic path length, small worldness, and average betweenness centrality have significant differences due to protocol and scanner vendor. Thus, there is a clear need to account for these site biases in connectivity analyses, or "harmonize" [26][27][28][29][30] .…”

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal

“…There are a growing number of multi-center studies that span multiple scanner, and acquisition protocols, or "sites". Unfortunately, these site specifications introduce significant confounding differences in images and downstream analysis such as diffusion tensor imaging (DTI) metrics and connectomics [11]- [15]. Thus, there is a growing number of methods designed to reduce site-related differences whilst preserving biological variability, or "harmonization" [16], [17].…”