کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6027927 1580918 2014 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Graph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
پیش نمایش صفحه اول مقاله
Graph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics
چکیده انگلیسی


- A study on mapping the brain connectome with resting-state ASL data was conducted.
- Region-wise small-world network properties using ASL were further investigated.
- Intrinsic relationships among network metrics and CBF have been investigated.
- We demonstrate the capability of ASL fMRI for mapping the brain connectome.
- We unravel nonlinear relationships among network metrics and CBF measurements.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 87, 15 February 2014, Pages 265-275
نویسندگان
, , ,