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Graph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neuroimage.2013.11.013 Byline: Xiaoyun Liang, Alan Connelly, Fernando Calamante Abstract: Human connectome mapping is important to understand both normal brain function and disease-related dysfunction. Althoug... Full description

Journal Title: Neuroimage Feb 15, 2014, Vol.87, p.265(11)
Main Author: Liang, Xiaoyun
Other Authors: Connelly, Alan , Calamante, Fernando
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Cengage Learning, Inc.
ID: ISSN: 1053-8119
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recordid: gale_ofa356763083
title: Graph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics
format: Article
creator:
  • Liang, Xiaoyun
  • Connelly, Alan
  • Calamante, Fernando
subjects:
  • Magnetic Resonance Imaging -- Analysis
ispartof: Neuroimage, Feb 15, 2014, Vol.87, p.265(11)
description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neuroimage.2013.11.013 Byline: Xiaoyun Liang, Alan Connelly, Fernando Calamante Abstract: Human connectome mapping is important to understand both normal brain function and disease-related dysfunction. Although blood-oxygen-level-dependent (BOLD) fMRI has been the most commonly used method for human connectome mapping, arterial spin labeling (ASL) is an fMRI technique to measure cerebral blood flow (CBF) directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity. In this study, investigations on properties of CBF networks using ASL perfusion data have been conducted on 10 healthy subjects. As with BOLD fMRI studies, the extracted networks exhibited small-world network properties. In addition, highly connected brain regions are shown to overlap mostly with hub regions detected from BOLD fMRI studies. Taken together, this demonstrates the capability of ASL fMRI for mapping the brain connectome. Furthermore, a sigmoid model was then employed to fit the extracted network metrics vs. CBF measurements. Interestingly, the relationships between 4 specific network metrics and region-wise CBF demonstrate that consistently nonlinear patterns exist across all subjects. In contrast to the positive nonlinear pattern of other network metrics (degree, vulnerability, and eigenvector centrality), the characteristic path length shows a negative nonlinear pattern, reflecting the mechanism underlying the small-world properties. To our knowledge, this is the first study to unravel the intrinsic relationships between specific network metrics and CBF estimates. This should have diagnostic and therapeutic implications for those studies focusing on patients who suffer from abnormal functional connectivity. Article History: Accepted 5 November 2013
language: English
source: Cengage Learning, Inc.
identifier: ISSN: 1053-8119
fulltext: fulltext
issn:
  • 1053-8119
  • 10538119
url: Link


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titleGraph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics
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descriptionTo link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neuroimage.2013.11.013 Byline: Xiaoyun Liang, Alan Connelly, Fernando Calamante Abstract: Human connectome mapping is important to understand both normal brain function and disease-related dysfunction. Although blood-oxygen-level-dependent (BOLD) fMRI has been the most commonly used method for human connectome mapping, arterial spin labeling (ASL) is an fMRI technique to measure cerebral blood flow (CBF) directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity. In this study, investigations on properties of CBF networks using ASL perfusion data have been conducted on 10 healthy subjects. As with BOLD fMRI studies, the extracted networks exhibited small-world network properties. In addition, highly connected brain regions are shown to overlap mostly with hub regions detected from BOLD fMRI studies. Taken together, this demonstrates the capability of ASL fMRI for mapping the brain connectome. Furthermore, a sigmoid model was then employed to fit the extracted network metrics vs. CBF measurements. Interestingly, the relationships between 4 specific network metrics and region-wise CBF demonstrate that consistently nonlinear patterns exist across all subjects. In contrast to the positive nonlinear pattern of other network metrics (degree, vulnerability, and eigenvector centrality), the characteristic path length shows a negative nonlinear pattern, reflecting the mechanism underlying the small-world properties. To our knowledge, this is the first study to unravel the intrinsic relationships between specific network metrics and CBF estimates. This should have diagnostic and therapeutic implications for those studies focusing on patients who suffer from abnormal functional connectivity. Article History: Accepted 5 November 2013
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descriptionTo link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neuroimage.2013.11.013 Byline: Xiaoyun Liang, Alan Connelly, Fernando Calamante Abstract: Human connectome mapping is important to understand both normal brain function and disease-related dysfunction. Although blood-oxygen-level-dependent (BOLD) fMRI has been the most commonly used method for human connectome mapping, arterial spin labeling (ASL) is an fMRI technique to measure cerebral blood flow (CBF) directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity. In this study, investigations on properties of CBF networks using ASL perfusion data have been conducted on 10 healthy subjects. As with BOLD fMRI studies, the extracted networks exhibited small-world network properties. In addition, highly connected brain regions are shown to overlap mostly with hub regions detected from BOLD fMRI studies. Taken together, this demonstrates the capability of ASL fMRI for mapping the brain connectome. Furthermore, a sigmoid model was then employed to fit the extracted network metrics vs. CBF measurements. Interestingly, the relationships between 4 specific network metrics and region-wise CBF demonstrate that consistently nonlinear patterns exist across all subjects. In contrast to the positive nonlinear pattern of other network metrics (degree, vulnerability, and eigenvector centrality), the characteristic path length shows a negative nonlinear pattern, reflecting the mechanism underlying the small-world properties. To our knowledge, this is the first study to unravel the intrinsic relationships between specific network metrics and CBF estimates. This should have diagnostic and therapeutic implications for those studies focusing on patients who suffer from abnormal functional connectivity. Article History: Accepted 5 November 2013
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abstractTo link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neuroimage.2013.11.013 Byline: Xiaoyun Liang, Alan Connelly, Fernando Calamante Abstract: Human connectome mapping is important to understand both normal brain function and disease-related dysfunction. Although blood-oxygen-level-dependent (BOLD) fMRI has been the most commonly used method for human connectome mapping, arterial spin labeling (ASL) is an fMRI technique to measure cerebral blood flow (CBF) directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity. In this study, investigations on properties of CBF networks using ASL perfusion data have been conducted on 10 healthy subjects. As with BOLD fMRI studies, the extracted networks exhibited small-world network properties. In addition, highly connected brain regions are shown to overlap mostly with hub regions detected from BOLD fMRI studies. Taken together, this demonstrates the capability of ASL fMRI for mapping the brain connectome. Furthermore, a sigmoid model was then employed to fit the extracted network metrics vs. CBF measurements. Interestingly, the relationships between 4 specific network metrics and region-wise CBF demonstrate that consistently nonlinear patterns exist across all subjects. In contrast to the positive nonlinear pattern of other network metrics (degree, vulnerability, and eigenvector centrality), the characteristic path length shows a negative nonlinear pattern, reflecting the mechanism underlying the small-world properties. To our knowledge, this is the first study to unravel the intrinsic relationships between specific network metrics and CBF estimates. This should have diagnostic and therapeutic implications for those studies focusing on patients who suffer from abnormal functional connectivity. Article History: Accepted 5 November 2013
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