| Literature DB >> 30488978 |
Ho-Ching Shawn Yang1, Zhenhu Liang1,2, Jinxia Fiona Yao1, Xin Shen1, Blaise deB Frederick3,4, Yunjie Tong1.
Abstract
The blood oxygen level-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) measures neuronal activation indirectly. Previous studies have found aperiodic, systemic low-frequency oscillations (sLFOs, ~0.1 Hz) in BOLD signals from resting state (RS) fMRI, which reflects the non-neuronal cerebral perfusion information. In this study, we investigated the possibility of extracting vascular information from the sLFOs in RS BOLD fMRI, which could provide complementary information to the neuronal activations. Two features of BOLD signals were exploited. First, time delays between the sLFOs of big blood vessels and brain voxels were calculated to determine cerebral circulation times and blood arrival times. Second, voxel-wise standard deviations (SD) of LFOs were calculated to represent the blood densities. We explored those features on the publicly available Myconnectome data set (a 2-year study of an individual subject (Male)), which contains 45 RS scans acquired after the subject had coffee, and 45 coffee-free RS scans, acquired on different days. Our results showed that shorter time delays and smaller SDs were detected in caffeinated scans. This is consistent with the vasoconstriction effects of caffeine, which leads to increased blood flow velocity. We also compared our results with previous findings on neuronal networks from the same data set. Our finding showed that brain regions with the significant vascular effect of caffeine coincide with those with a significant neuronal effect, indicating close interaction. This study provides methods to assess the physiological information from RS fMRI. Together with the neuronal information, we can study simultaneously the underlying correlations and interactions between vascular and neuronal networks, especially in pharmacological studies.Entities:
Keywords: blood vessels; cerebral blood flow; cerebral cortex; magnetic resonance imaging
Year: 2018 PMID: 30488978 PMCID: PMC6367009 DOI: 10.1002/jnr.24360
Source DB: PubMed Journal: J Neurosci Res ISSN: 0360-4012 Impact factor: 4.164