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Challenges and Opportunities in dMRI Data Harmonization

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Computational Diffusion MRI (MICCAI 2019)

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

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.

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Notes

  1. 1.

    https://grants.nih.gov/reproducibility/index.htm.

  2. 2.

    This assumption is not necessarily true for either type of phantom: for the human phantom, slight brain morphometry changes have been observed in the course of a day [45], and repeat scans on the same scanner will not be identical; for a manufactured phantom, transportation of the phantom may affect its geometry.

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Acknowledgements

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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Authors and Affiliations

  1. Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, 90292, USA

    Alyssa H. Zhu, Daniel C. Moyer, Talia M. Nir, Paul M. Thompson & Neda Jahanshad

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  1. Alyssa H. Zhu
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  2. Daniel C. Moyer
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  3. Talia M. Nir
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  4. Paul M. Thompson
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  5. Neda Jahanshad
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Correspondence to Neda Jahanshad .

Editor information

Editors and Affiliations

  1. Centre for Medical Image Computing, University College London, London, UK

    Elisenda Bonet-Carne

  2. Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK

    Francesco Grussu

  3. Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Lipeng Ning

  4. Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Farshid Sepehrband

  5. Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK

    Chantal M. W. Tax

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Zhu, A.H., Moyer, D.C., Nir, T.M., Thompson, P.M., Jahanshad, N. (2019). Challenges and Opportunities in dMRI Data Harmonization. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_13

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