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dc.contributor.authorLiang, Xiaoyunen
dc.contributor.authorConnelly, Alanen
dc.contributor.authorCalamante, Fernandoen
dc.identifier.citationNeuroimage 2013; 87(): 265-75en
dc.description.abstractHuman 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.en
dc.subject.otherArterial spin labelingen
dc.subject.otherFunctional connectivityen
dc.subject.otherNetwork metricsen
dc.subject.otherSmall-world networksen
dc.subject.otherBrain.blood supplyen
dc.subject.otherCerebrovascular Circulation.physiologyen
dc.subject.otherImage Processing, Computer-Assisted.methodsen
dc.subject.otherMagnetic Resonance Imaging.methodsen
dc.subject.otherNonlinear Dynamicsen
dc.subject.otherSpin Labelsen
dc.titleGraph analysis of resting-state ASL perfusion MRI data: nonlinear correlations among CBF and network metrics.en
dc.typeJournal Articleen
dc.identifier.affiliationFlorey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationDepartment of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationDepartment of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australiaen
dc.type.austinJournal Articleen
item.openairetypeJournal Article-
item.fulltextNo Fulltext-
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