Multicenter data harmonization for regional brain atrophy and application in multiple sclerosis

Background: In multiple sclerosis (MS), determination of regional brain atrophy is clinically relevant. However, analysis of large datasets is rare because of the increased variability in multicenter data. Purpose: To compare different methods to correct for center effects. To investigate regional gray matter (GM) volume in relapsing-remitting MS in a large multicenter dataset. Methods: MRI scans of 466 MS patients and 279 healthy controls (HC) were retrieved from the Italian Neuroimaging Network Initiative repository. Voxel-based morphometry was performed. The center effect was accounted for with different methods: (a) no correction, (b) factor in the statistical model, (c) ComBat method and (d) subsampling procedure to match single-center distributions. By applying the best correction method, GM atrophy was assessed in MS patients vs HC and according to clinical disability, disease duration and T2 lesion volume. Results were assessed voxel-wise using general linear model. Results: The average residuals for the harmonization methods were 5.03 (a), 4.42 (b), 4.26 (c) and 2.98 (d). The comparison between MS patients and HC identified thalami and other deep GM nuclei, the cerebellum and several cortical regions. At single-center analysis, the thalami were always involved, whereas different other regions were found in each center. Cerebellar atrophy correlated with clinical disability, while deep GM nuclei atrophy correlated with T2-lesion volume. Conclusion: Harmonization based on subsampling more effectively decreased the residuals of the statistical model applied. In comparison with findings from single-center analysis, the multicenter results were more robust, highlighting the importance of data repositories from multiple centers.