Abstract
Diffusion weighted imaging (DWI) allows investigators to identify microstructural differences between subjects, but variability due to session and scanner biases is still a challenge. To investigate DWI variability, we present MASiVar, a multisite dataset consisting of 319 diffusion scans acquired at 3T from b = 1000 to 3000 s/mm2 across 97 different healthy subjects and four different scanners as a publicly available, preprocessed, and de-identified dataset. With these data we characterize variability on the intrasession intrascanner (N = 158), intersession intrascanner (N = 328), intersession interscanner (N = 53), and intersubject intrascanner (N = 80) levels. Our baseline analysis focuses on four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy (FA), mean diffusivity, and principal eigenvector; region-wise cerebral spinal fluid volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, length and FA; and connectome correlation and maximized modularity, global efficiency, and characteristic path length. We plot the scan/re-scan discrepancies in these measures at each level and find that variability generally increases with intrasession to intersession to interscanner to intersubject effects and that sometimes interscanner variability can approach intersubject variability. This baseline study suggests harmonization between scanners for multisite analyses is critical prior to inference of group differences on subjects and demonstrates the potential of MASiVar to investigate DWI variability across multiple levels and processing approaches simultaneously.
Competing Interest Statement
The authors have declared no competing interest.
