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

Journal article


Liang, Xiaoyun, Connelly, Alan and Calamante, Fernando. (2014) Graph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics. NeuroImage. 87, pp. 265 - 275. https://doi.org/10.1016/j.neuroimage.2013.11.013
AuthorsLiang, Xiaoyun, Connelly, Alan and Calamante, Fernando
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.

Keywordssmall-world networks; arterial spin labeling; functional connectivity; network metrics; connectome
Year2014
JournalNeuroImage
Journal citation87, pp. 265 - 275
PublisherElsevier BV
ISSN1053-8119
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neuroimage.2013.11.013
Scopus EID2-s2.0-84890903014
Page range265 - 275
Research GroupMary MacKillop Institute for Health Research
Place of publicationUnited States of America
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