Challenges and Opportunities in dMRI Data Harmonization

  • Alyssa H. Zhu
  • Daniel C. Moyer
  • Talia M. Nir
  • Paul M. Thompson
  • Neda JahanshadEmail author
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Advances in diffusion MRI (dMRI) have led to discoveries of factors that affect brain microstructure and connectivity in health and disease. The small size of many neuroimaging studies led to concerns about poor reproducibility of research findings, and calls for the comparison and pooling of multi-cohort datasets to establish the consistency of reported effects. Across studies diffusion MRI protocols vary in spatial, angular and q-space resolution, b-value, as well as hardware used—all of which affect measured diffusion parameters. Efforts to compare and pool dMRI measures use meta- or mega- analytical techniques to compensate for these sources of variance. Meta-analytical methods gauge the consistency of effects, and mega-analytical methods involve mathematical or statistical transformations of the data. Here, we review some recent advances that allowed the diffusion community to create large scale population studies with greater rigor and generalizability than was previously attainable by individual studies.


Multi-site Harmonization diffusion MRI DTI DWI 



The work was supported in part by U54 EB020403. Additional support was provided by R01MH116147, P41 EB015922, RF1 AG051710, RF1 AG041915 and and Michael J. Fox Foundation grant 14848.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alyssa H. Zhu
    • 1
  • Daniel C. Moyer
    • 1
  • Talia M. Nir
    • 1
  • Paul M. Thompson
    • 1
  • Neda Jahanshad
    • 1
    Email author
  1. 1.Imaging Genetics Center, Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of the University of Southern CaliforniaMarina del ReyUSA

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