This title appears in the Scientific Report :
2024
Please use the identifier:
http://dx.doi.org/10.1016/j.neuroimage.2024.120595 in citations.
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2024-02512 in citations.
GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox
GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox
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 modal...
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Personal Name(s): | Park, Yeongjun |
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Lee, Mi Ji / Yoo, Seulki / Kim, Chae Yeon / Namgung, Jong Young / Park, Yunseo / Park, Hyunjin / Lee, Eun-Chong / Yoon, Yeo Dong / Paquola, Casey / Bernhardt, Boris C. / Park, Bo-yong (Corresponding author) | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Published in: | NeuroImage, 291 (2024) S. 120595 - |
Imprint: |
Orlando, Fla.
Academic Press
2024
|
DOI: |
10.1016/j.neuroimage.2024.120595 |
DOI: |
10.34734/FZJ-2024-02512 |
Document Type: |
Journal Article |
Research Program: |
Brain Dysfunction and Plasticity Neuroscientific Data Analytics and AI |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2024-02512 in citations.
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. |