Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study
•A new index was developed to quantify brain SC-FC coupling and assess network topological similarity.•Cortical SC-FC coupling was strongest in the visual network and weakest in the ventral attention network.•In the cortex, SC-FC coupling strength declines with age and positively correlates with cog...
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| Published in: | NeuroImage (Orlando, Fla.) Vol. 299; p. 120847 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.10.2024
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| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
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| Abstract | •A new index was developed to quantify brain SC-FC coupling and assess network topological similarity.•Cortical SC-FC coupling was strongest in the visual network and weakest in the ventral attention network.•In the cortex, SC-FC coupling strength declines with age and positively correlates with cognitive function.
Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly. |
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| AbstractList | Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly. Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly. •A new index was developed to quantify brain SC-FC coupling and assess network topological similarity.•Cortical SC-FC coupling was strongest in the visual network and weakest in the ventral attention network.•In the cortex, SC-FC coupling strength declines with age and positively correlates with cognitive function. Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly. |
| ArticleNumber | 120847 |
| Author | Liu, Hao Sachdev, Perminder Wang, Suying Kochan, Nicole Li, Hao Wen, Wei Jing, Jing Wang, Yilong Jiang, Yong Li, Zixiao Jiang, Jiyang Meng, Xia Pan, Yuesong Niu, Haijun Zhu, Wanlin Zhang, Jicong Zheng, Huaguang Cai, Xueli Brodaty, Henry Zhou, Yijun Wei, Tiemin Liu, Tao Wang, Yongjun Liu, Chang |
| Author_xml | – sequence: 1 givenname: Chang surname: Liu fullname: Liu, Chang organization: Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China – sequence: 2 givenname: Jing surname: Jing fullname: Jing, Jing email: jingj_bjttyy@163.com organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 3 givenname: Jiyang orcidid: 0000-0002-2147-6302 surname: Jiang fullname: Jiang, Jiyang organization: Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia – sequence: 4 givenname: Wei surname: Wen fullname: Wen, Wei organization: Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia – sequence: 5 givenname: Wanlin surname: Zhu fullname: Zhu, Wanlin organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 6 givenname: Zixiao surname: Li fullname: Li, Zixiao organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 7 givenname: Yuesong surname: Pan fullname: Pan, Yuesong organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 8 givenname: Xueli surname: Cai fullname: Cai, Xueli organization: Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China – sequence: 9 givenname: Hao surname: Liu fullname: Liu, Hao organization: Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China – sequence: 10 givenname: Yijun surname: Zhou fullname: Zhou, Yijun organization: Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China – sequence: 11 givenname: Xia surname: Meng fullname: Meng, Xia organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 12 givenname: Jicong surname: Zhang fullname: Zhang, Jicong email: jicongzhang@buaa.edu.cn organization: Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China – sequence: 13 givenname: Yilong surname: Wang fullname: Wang, Yilong organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 14 givenname: Hao surname: Li fullname: Li, Hao organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 15 givenname: Yong surname: Jiang fullname: Jiang, Yong organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 16 givenname: Huaguang surname: Zheng fullname: Zheng, Huaguang organization: China National Clinical Research Center for Neurological Diseases, Beijing, China – sequence: 17 givenname: Suying surname: Wang fullname: Wang, Suying organization: Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China – sequence: 18 givenname: Haijun surname: Niu fullname: Niu, Haijun organization: Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China – sequence: 19 givenname: Nicole orcidid: 0000-0002-8630-6398 surname: Kochan fullname: Kochan, Nicole organization: Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia – sequence: 20 givenname: Henry orcidid: 0000-0001-9487-6617 surname: Brodaty fullname: Brodaty, Henry organization: Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia – sequence: 21 givenname: Tiemin surname: Wei fullname: Wei, Tiemin organization: Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China – sequence: 22 givenname: Perminder orcidid: 0000-0002-9595-3220 surname: Sachdev fullname: Sachdev, Perminder organization: Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia – sequence: 23 givenname: Tao orcidid: 0000-0002-7783-3073 surname: Liu fullname: Liu, Tao email: tao.liu@buaa.edu.cn organization: Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China – sequence: 24 givenname: Yongjun surname: Wang fullname: Wang, Yongjun email: yongjunwang@ncrcnd.org.cn organization: China National Clinical Research Center for Neurological Diseases, Beijing, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39265959$$D View this record in MEDLINE/PubMed |
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| Keywords | Structure-function coupling Cognition Brain mechanisms cognition brain mechanisms structure-function coupling |
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