Diffusion time-related structure-function coupling reveals differential association with inter-individual variations in body mass index
•Structure-function coupling related to body mass index (BMI) was investigated.•We simulated functional connectivity from structural data using Riemannian approach.•BMI-related alterations in coupling were most clear when the hierarchy was evident.•Alterations were most obvious in the transmodal reg...
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| Veröffentlicht in: | NeuroImage (Orlando, Fla.) Jg. 291; S. 120590 |
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| Abstract | •Structure-function coupling related to body mass index (BMI) was investigated.•We simulated functional connectivity from structural data using Riemannian approach.•BMI-related alterations in coupling were most clear when the hierarchy was evident.•Alterations were most obvious in the transmodal regions.•Cytoarchitecture and transcriptomic analyses provided biological underpinnings.
Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. 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 functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the 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 the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI. |
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| AbstractList | •Structure-function coupling related to body mass index (BMI) was investigated.•We simulated functional connectivity from structural data using Riemannian approach.•BMI-related alterations in coupling were most clear when the hierarchy was evident.•Alterations were most obvious in the transmodal regions.•Cytoarchitecture and transcriptomic analyses provided biological underpinnings.
Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. 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 functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the 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 the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI. Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. 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 functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the 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 the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI. Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. 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 functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the 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 the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI.Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. 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 functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the 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 the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI. |
| ArticleNumber | 120590 |
| Author | Park, Yunseo Kim, Chae Yeon Namgung, Jong Young Park, Bo-yong Park, Yeongjun |
| Author_xml | – sequence: 1 givenname: Jong Young orcidid: 0009-0003-6822-3112 surname: Namgung fullname: Namgung, Jong Young organization: Department of Data Science, Inha University, Incheon, Republic of Korea – sequence: 2 givenname: Yeongjun surname: Park fullname: Park, Yeongjun organization: Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea – sequence: 3 givenname: Yunseo surname: Park fullname: Park, Yunseo organization: Department of Data Science, Inha University, Incheon, Republic of Korea – sequence: 4 givenname: Chae Yeon surname: Kim fullname: Kim, Chae Yeon organization: Department of Data Science, Inha University, Incheon, Republic of Korea – sequence: 5 givenname: Bo-yong orcidid: 0000-0001-7096-337X surname: Park fullname: Park, Bo-yong email: boyong.park@inha.ac.kr organization: Department of Data Science, Inha University, Incheon, Republic of Korea |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38548036$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1002_hbm_70019 crossref_primary_10_1016_j_nicl_2025_103817 |
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| Keywords | Structure-function coupling Body mass index Diffusion time Synaptic communication Riemannian optimization synaptic communication diffusion time body mass index |
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| Title | Diffusion time-related structure-function coupling reveals differential association with inter-individual variations in body mass index |
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