Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/11947
Title: Graph analysis of resting-state ASL perfusion MRI data: nonlinear correlations among CBF and network metrics.
Authors: Liang, Xiaoyun;Connelly, Alan;Calamante, Fernando
Affiliation: Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Electronic address: x.liang@brain.org.au.
Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
Issue Date: 16-Nov-2013
Citation: Neuroimage 2013; 87(): 265-75
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.
Internal ID Number: 24246488
URI: http://ahro.austin.org.au/austinjspui/handle/1/11947
DOI: 10.1016/j.neuroimage.2013.11.013
URL: http://www.ncbi.nlm.nih.gov/pubmed/24246488
Type: Journal Article
Subjects: Arterial spin labeling
Connectome
Functional connectivity
Network metrics
Small-world networks
Brain.blood supply
Cerebrovascular Circulation.physiology
Connectome.methods
Humans
Image Processing, Computer-Assisted.methods
Magnetic Resonance Imaging.methods
Nonlinear Dynamics
Spin Labels
Appears in Collections:Journal articles

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