Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease.
Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have b...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005550&type=printable |
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| author | Maxwell B Wang Julia P Owen Pratik Mukherjee Ashish Raj |
| author_facet | Maxwell B Wang Julia P Owen Pratik Mukherjee Ashish Raj |
| author_sort | Maxwell B Wang |
| collection | DOAJ |
| description | Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome. |
| format | Article |
| id | doaj-art-eef006fd4d064b62b54980cac1b66a64 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2017-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-eef006fd4d064b62b54980cac1b66a642025-08-20T03:11:25ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-06-01136e100555010.1371/journal.pcbi.1005550Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease.Maxwell B WangJulia P OwenPratik MukherjeeAshish RajRecent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005550&type=printable |
| spellingShingle | Maxwell B Wang Julia P Owen Pratik Mukherjee Ashish Raj Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. PLoS Computational Biology |
| title | Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. |
| title_full | Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. |
| title_fullStr | Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. |
| title_full_unstemmed | Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. |
| title_short | Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. |
| title_sort | brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005550&type=printable |
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