Improved Injury Detection Through Harmonizing Multi-Site Neuroimaging Data after Experimental TBI: A Translational...

Multi-site neuroimaging studies have become increasingly common in order to generate larger samples of reproducible data to answer questions associated with smaller effect sizes. The data harmonization model NeuroCombat has been shown to remove site effects introduced by differences in site-related technical variance while maintaining group differences, yet its effect on improving statistical power in pre-clinical models of CNS disease is unclear. The present study examined fractional anisotropy data computed from diffusion weighted imaging data at 3 and 30 days post-controlled cortical impact injury from 184 adult rats across four sites as part of the Translational-Outcome-Project-in-Neurotrauma (TOP-NT) Consortium. Findings confirmed prior clinical reports that NeuroCombat fails to remove site effects in data containing a high proportion-of-outliers (>5%) and skewness, which introduced significant variation in non-outlier sites. After removal of one outlier site and harmonization using a global sham population, harmonization displayed an increase in effect size in data that displayed group level effects (p<0.01) in both univariate and voxel-level volumes of pathology. This was characterized by movement toward similar distributions in voxel measurements (Kolmogorov-Smirnov p<<0.001 to >0.01) and statistical power increases within the ipsilateral cortex. Harmonization improved statistical power and frequency of significant differences in areas with existing group differences, thus improving the ability to detect regions affected by injury rather than by other confounds. These findings indicate the utility of NeuroCombat in reproducible data collection, where biological differences can be accurately revealed to allow for greater reliability in multi-site neuroimaging studies.