Harmonization of Multi-Site DTI and NODDI Data Using the Combined Association Test [Proceedings of the 2022 Young Investigator Award]

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  • SAITO Yuya
    Department of Radiology, Juntendo University Graduate School of Medicine
  • KAMAGATA Koji
    Department of Radiology, Juntendo University Graduate School of Medicine
  • MAIKUSA Norihide
    Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
  • ANDICA Christina
    Department of Radiology, Juntendo University Graduate School of Medicine
  • UCHIDA Wataru
    Department of Radiology, Juntendo University Graduate School of Medicine
  • NOZAKI Hayato
    Department of Radiology, Juntendo University Graduate School of Medicine Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University
  • OWAKI Mana
    Department of Radiology, Juntendo University Graduate School of Medicine Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University
  • HAGIWARA Akifumi
    Department of Radiology, Juntendo University Graduate School of Medicine
  • FUJITA Shohei
    Department of Radiology, Juntendo University Graduate School of Medicine
  • AKASHI Toshiaki
    Department of Radiology, Juntendo University Graduate School of Medicine
  • WADA Akihiko
    Department of Radiology, Juntendo University Graduate School of Medicine
  • KOIKE Shinsuke
    Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
  • HORI Masaaki
    Department of Radiology, Toho University Omori Medical Center
  • AOKI Shigeki
    Department of Radiology, Juntendo University Graduate School of Medicine

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  • ComBatを用いた多施設DTIおよびNODDI定量値のハーモナイゼーション［国際飛躍賞記録］

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Abstract

<p>　When analyzing the multi-site diffusion MRI (dMRI) data, metrics should be harmonized to remove the site effect. In this study, we applied the combined association test (ComBat), which used the regression of covariates with an empirical Bayes framework for diffusion metrics based on diffusion tensor imaging (DTI), neurite orientation dispersion, and density imaging (NODDI). The results showed that the ComBat could harmonize the site-related effects in DTI and NODDI metrics based on multi-site dMRI while preserving the biological information of the participant, such as sex differences and correlation with age. Thus, the ComBat could be applied in large multi-site studies to identify subtle white matter changes.</p>

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