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Local connectome phenotypes predict social, health, and cognitive factors
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| Zeitschriftentitel: | Network Neuroscience |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Network Neuroscience, 2, 2018, 1, S. 86-105 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MIT Press
|
| Schlagwörter: |
| author_facet |
Powell, Michael A. Garcia, Javier O. Yeh, Fang-Cheng Vettel, Jean M. Verstynen, Timothy Powell, Michael A. Garcia, Javier O. Yeh, Fang-Cheng Vettel, Jean M. Verstynen, Timothy |
|---|---|
| author |
Powell, Michael A. Garcia, Javier O. Yeh, Fang-Cheng Vettel, Jean M. Verstynen, Timothy |
| spellingShingle |
Powell, Michael A. Garcia, Javier O. Yeh, Fang-Cheng Vettel, Jean M. Verstynen, Timothy Network Neuroscience Local connectome phenotypes predict social, health, and cognitive factors Applied Mathematics Artificial Intelligence Computer Science Applications General Neuroscience |
| author_sort |
powell, michael a. |
| spelling |
Powell, Michael A. Garcia, Javier O. Yeh, Fang-Cheng Vettel, Jean M. Verstynen, Timothy 2472-1751 MIT Press Applied Mathematics Artificial Intelligence Computer Science Applications General Neuroscience http://dx.doi.org/10.1162/netn_a_00031 <jats:p> The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. </jats:p> Local connectome phenotypes predict social, health, and cognitive factors Network Neuroscience |
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| title |
Local connectome phenotypes predict social, health, and cognitive factors |
| title_unstemmed |
Local connectome phenotypes predict social, health, and cognitive factors |
| title_full |
Local connectome phenotypes predict social, health, and cognitive factors |
| title_fullStr |
Local connectome phenotypes predict social, health, and cognitive factors |
| title_full_unstemmed |
Local connectome phenotypes predict social, health, and cognitive factors |
| title_short |
Local connectome phenotypes predict social, health, and cognitive factors |
| title_sort |
local connectome phenotypes predict social, health, and cognitive factors |
| topic |
Applied Mathematics Artificial Intelligence Computer Science Applications General Neuroscience |
| url |
http://dx.doi.org/10.1162/netn_a_00031 |
| publishDate |
2018 |
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86-105 |
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<jats:p> The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. </jats:p> |
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| author | Powell, Michael A., Garcia, Javier O., Yeh, Fang-Cheng, Vettel, Jean M., Verstynen, Timothy |
| author_facet | Powell, Michael A., Garcia, Javier O., Yeh, Fang-Cheng, Vettel, Jean M., Verstynen, Timothy, Powell, Michael A., Garcia, Javier O., Yeh, Fang-Cheng, Vettel, Jean M., Verstynen, Timothy |
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| description | <jats:p> The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. </jats:p> |
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| spelling | Powell, Michael A. Garcia, Javier O. Yeh, Fang-Cheng Vettel, Jean M. Verstynen, Timothy 2472-1751 MIT Press Applied Mathematics Artificial Intelligence Computer Science Applications General Neuroscience http://dx.doi.org/10.1162/netn_a_00031 <jats:p> The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. </jats:p> Local connectome phenotypes predict social, health, and cognitive factors Network Neuroscience |
| spellingShingle | Powell, Michael A., Garcia, Javier O., Yeh, Fang-Cheng, Vettel, Jean M., Verstynen, Timothy, Network Neuroscience, Local connectome phenotypes predict social, health, and cognitive factors, Applied Mathematics, Artificial Intelligence, Computer Science Applications, General Neuroscience |
| title | Local connectome phenotypes predict social, health, and cognitive factors |
| title_full | Local connectome phenotypes predict social, health, and cognitive factors |
| title_fullStr | Local connectome phenotypes predict social, health, and cognitive factors |
| title_full_unstemmed | Local connectome phenotypes predict social, health, and cognitive factors |
| title_short | Local connectome phenotypes predict social, health, and cognitive factors |
| title_sort | local connectome phenotypes predict social, health, and cognitive factors |
| title_unstemmed | Local connectome phenotypes predict social, health, and cognitive factors |
| topic | Applied Mathematics, Artificial Intelligence, Computer Science Applications, General Neuroscience |
| url | http://dx.doi.org/10.1162/netn_a_00031 |