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@ARTICLE{Park:1024846,
      author       = {Park, Yeongjun and Lee, Mi Ji and Yoo, Seulki and Kim, Chae
                      Yeon and Namgung, Jong Young and Park, Yunseo and Park,
                      Hyunjin and Lee, Eun-Chong and Yoon, Yeo Dong and Paquola,
                      Casey and Bernhardt, Boris C. and Park, Bo-yong},
      title        = {{GAN}-{MAT}: {G}enerative adversarial network-based
                      microstructural profile covariance analysis toolbox},
      journal      = {NeuroImage},
      volume       = {291},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2024-02512},
      pages        = {120595 -},
      year         = {2024},
      abstract     = {Multimodal magnetic resonance imaging (MRI) provides
                      complementary information for investigating brain structure
                      and function; for example, an in vivo
                      microstructure-sensitive proxy can be estimated using the
                      ratio between T1- and T2-weighted structural MRI. However,
                      acquiring multiple imaging modalities is challenging in
                      patients with inattentive disorders. In this study, we
                      proposed a comprehensive framework to provide multiple
                      imaging features related to the brain microstructure using
                      only T1-weighted MRI. Our toolbox consists of (i)
                      synthesizing T2-weighted MRI from T1-weighted MRI using a
                      conditional generative adversarial network; (ii) estimating
                      microstructural features, including intracortical covariance
                      and moment features of cortical layer-wise microstructural
                      profiles; and (iii) generating a microstructural gradient,
                      which is a low-dimensional representation of the
                      intracortical microstructure profile. We trained and tested
                      our toolbox using T1- and T2-weighted MRI scans of 1,104
                      healthy young adults obtained from the Human Connectome
                      Project database. We found that the synthesized T2-weighted
                      MRI was very similar to the actual image and that the
                      synthesized data successfully reproduced the microstructural
                      features. The toolbox was validated using an independent
                      dataset containing healthy controls and patients with
                      episodic migraine as well as the atypical developmental
                      condition of autism spectrum disorder. Our toolbox may
                      provide a new paradigm for analyzing multimodal structural
                      MRI in the neuroscience community.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38554782},
      UT           = {WOS:001218479400001},
      doi          = {10.1016/j.neuroimage.2024.120595},
      url          = {https://juser.fz-juelich.de/record/1024846},
}