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GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox

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Title
GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox
Author(s)
Park, Yeongjun; Lee, Mi Ji; Yoo, Seulki; Kim, Chae Yeon; Namgung, Jong Young; Park, Yunseo; Hyunjin Park; Lee, Eun-Chong; Yoon, Yeo Dong; Paquola, Casey; Bernhardt, Boris C.; Bo-yong Park
Publication Date
2024-05
Journal
NeuroImage, v.291
Publisher
Academic Press Inc.
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 and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT. © 2024 The Author(s)
URI
https://pr.ibs.re.kr/handle/8788114/15180
DOI
10.1016/j.neuroimage.2024.120595
ISSN
1053-8119
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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