Multisite Harmonization of Structural DTI Networks in Children: An A-CAP Study

Abstract

The analysis of large, multisite neuroimaging datasets provides a promising means for robust characterization of brain networks that can reduce false positives and improve reproducibility. However, the use of different MRI scanners introduces variability to the data. Managing those sources of variability is increasingly important for the generation of accurate group-level inferences. ComBat is one of the most promising tools for multisite (multiscanner) harmonization of structural neuroimaging data, but no study has examined its application to graph theory metrics derived from the structural brain connectome. The present work evaluates the use of ComBat for multisite harmonization in the context of structural network analysis of diffusion-weighted scans from the Advancing Concussion Assessment in Pediatrics (A-CAP) study. Scans were acquired on six different scanners from 484 children aged 8.00–16.99 years [Mean = 12.37 ± 2.34 years; 289 (59.7%) Male] ~10 days following mild traumatic brain injury (n = 313) or orthopedic injury (n = 171). Whole brain deterministic diffusion tensor tractography was conducted and used to construct a 90 x 90 weighted (average fractional anisotropy) adjacency matrix for each scan. ComBat harmonization was applied separately at one of two different stages during data processing, either on the (i) weighted adjacency matrices (matrix harmonization) or (ii) global network metrics derived using unharmonized weighted adjacency matrices (parameter harmonization). Global network metrics based on unharmonized adjacency matrices and each harmonization approach were derived. Robust scanner effects were found for unharmonized metrics. Some scanner effects remained significant for matrix harmonized metrics, but effect sizes were less robust. Parameter harmonized metrics did not differ by scanner. Intraclass correlations (ICC) indicated good to excellent within-scanner consistency between metrics calculated before and after both harmonization approaches. Age correlated with unharmonized network metrics, but was more strongly correlated with network metrics based on both harmonization approaches. Parameter harmonization successfully controlled for scanner variability while preserving network topology and connectivity weights, indicating that harmonization of global network parameters based on unharmonized adjacency matrices may provide optimal results. The current work supports the use of ComBat for removing multiscanner effects on global network topology.

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

“…One limitation to this work is that we only considered two harmonization techniques: voxel-wise and feature-wise. There is evidence that ComBat harmonization on connectivity matrices prior to graph measure computation may also be effective [8]. Another limitation is that NSDN was not trained with data specific to either the VMAP or BLSA sites, which may increase performance.…”

“…Despite these advances, statistically significant confounding differences in images caused by data acquisition and scanner noise is an established problem in multi-site DWI analysis [6], [7]. These differences carryover to graph theory analysis and other downstream tasks [8]. For instance, in the cohort investigated presently, there is clear differentiation between data distributions collected on different scanners (Figure 1).…”

“…The algorithm uses an empirical Bayes framework to improve the variance for multiplicative and additive scanner effects [9]. Previous work suggests that ComBat is an effective tool for harmonizing differences between connectivity matrices as well as the graph measures themselves [8]. In this paper we apply ComBat on the graph measure distributions to perform feature-wise harmonization.…”

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

“…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] .…”

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

“…One study applies ComBat on connectivity matrices weighted by average FA. 19 The researchers indicate that implementing ComBat on global parameters is more costefficient and yields superior results compared with applying it on connectivity matrices. 19 We seek to verify their findings on connectivity matrices weighted by number of streamlines and mean streamline length and evaluate two more methods of matrix harmonization.…”