Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization

de Brito Robalo, Bruno M, de Luca, Alberto, Chen, Christopher, Dewenter, Anna, Duering, Marco, Hilal, Saima, Koek, Huiberdina L, Kopczak, Anna, Lam, Bonnie Yin Ka, Leemans, Alexander, Mok, Vincent, Onkenhout, Laurien P, van den Brink, Hilde, Biessels, Geert Jan


Neuroimage: Clinical [E] 36 p. 103217


PURPOSE: To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets.

METHODS: Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference.

RESULTS: In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97).

CONCLUSION: We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.