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.

Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated inter-individual variations in BMI to be associated with altered brain structure and function. However, how the structure-function correspondence is altered according to BMI is under-investigated. In this study, we combined structural and functional connectivity using Riemannian optimization with varying diffusion time parameters and assessed their association with BMI. First, we simulated functional connectivity from structural connectivity and generated low-dimensional principal gradients of the simulated functional connectivity across diffusion times, where low and high diffusion times indirectly reflected mono- and polysynaptic communication. We found the most apparent cortical hierarchical organization differentiating between low-level sensory and higher-order transmodal regions in the middle of the diffusion time, indicating that the hierarchical organization of the brain may reflect the intermediate mechanisms of mono- and polysynaptic communications. Associations between the simulated gradients and BMI revealed the strongest relationship when the hierarchical structure was most evident. Moreover, the functional gradient-BMI association map showed significant correlations with the cytoarchitectonic measures of the microstructural gradient and moment features, indicating that BMI-related functional connectome alterations were remarkable in higher-order cognitive control-related brain regions. Finally, transcriptomic association analysis provided potential biological underpinnings, specifying gene enrichment in the striatum, hypothalamus, and cortical cells. Our findings provide evidence that structure-function correspondence is strongly coupled with BMI when hierarchical organization is most apparent, and the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI.