机构：[a]Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China[b]Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China[c]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China放射科首都医科大学宣武医院[d]Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China[e]McGovern Institute for Brain Research, Peking University, Beijing, 100871, China[f]Shenzhen Key Laboratory of Affective and Social Cognitive Science, Institute of Affective and Social Neuroscience, Shenzhen University, Shenzhen, 518060, China[g]Shenzhen Institute of Neuroscience, Shenzhen, 518057, China
Traditional resting-state functional magnetic resonance imaging (fMRI) is mainly based on the blood oxygenation level-dependent (BOLD) contrast. The oxygen extraction fraction (OEF) represents an important parameter of brain metabolism and is a key biomarker of tissue viability, detecting the ratio of oxygen utilization to oxygen delivery. Investigating spontaneous fluctuations in the OEF-weighted signal is crucial for understanding the underlying mechanism of brain activity because of the immense energy budget during the resting state. However, due to the poor temporal resolution of OEF mapping, no studies have reported using OEF contrast to assess resting-state brain activity. In this fMRI study, we recorded brain OEF-weighted fluctuations for 10?min in healthy volunteers across two scanning visits, using our recently developed pulse sequence that can acquire whole-brain voxel-wise OEF-weighted signals with a temporal resolution of 3?s. Using both group-independent component analysis and seed-based functional connectivity analysis, we robustly identified intrinsic brain networks, including the medial visual, lateral visual, auditory, default mode and bilateral executive control networks, using OEF contrast. Furthermore, we investigated the resting-state local characteristics of brain activity based on OEF-weighted signals using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). We demonstrated that the gray matter regions of the brain, especially those in the default mode network, showed higher ReHo and fALFF values with the OEF contrast. Moreover, voxel-wise test-retest reliability comparisons across the whole brain demonstrated that the reliability of resting-state brain activity based on the OEF contrast was moderate for the network indices and high for the local activity indices, especially for ReHo. Although the reliabilities of the OEF-based indices were generally lower than those based on BOLD, the reliability of OEF-ReHo was slightly higher than that of BOLD-ReHo, with a small effect size, which indicated that OEF-ReHo could be used as a reliable index for characterizing resting-state local brain activity as a complement to BOLD. In conclusion, OEF can be used as an effective contrast to study resting-state brain activity with a medium to high test-retest reliability.
Copyright ? 2019 Elsevier Inc. All rights reserved.
基金：
This work was supported by National Key Research and Development
Program of China (grant numbers 2018YFC2000603 and
2017YFC0108900); the China's National Strategic Basic Research Program
(“973”) (grant number 2015CB856400); the National Natural Science
Foundation of China (grant numbers 81871427, 81671765, 81430037,
81727808, 81790650, 81790651, 31421003, and 81522021); the Beijing
Municipal Natural Science Foundation (grant number 7172121); the Beijing
Municipal Science and Technology Commission (grant numbers
Z181100001518005, Z161100002616006, and Z171100000117012); the
Shenzhen Peacock Plan (grant number KQTD2015033016104926); the
Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team
(grant number 2016ZT06S220); and the Shenzhen Science and Technology
Research Funding Program(grant number JCYJ20170412164413575)
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2018]版：
大类|2 区医学
小类|1 区神经成像2 区神经科学2 区核医学
最新[2025]版：
大类|2 区医学
小类|1 区神经成像2 区神经科学2 区核医学
JCR分区：
出版当年[2017]版：
Q1NEUROSCIENCESQ1NEUROIMAGINGQ1RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版：
Q1NEUROIMAGINGQ1NEUROSCIENCESQ1RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
第一作者机构：[a]Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China[b]Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
共同第一作者：
通讯作者：
通讯机构：[*1]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.[*2]Center for MRI Research, Peking University, Beijing, 100871, China.[*3]Center for MRI Research, Peking University, Beijing, 100871, China.
推荐引用方式(GB/T 7714)：
Yang Yang,Yayan Yin,Jie Lu,et al.Detecting resting-state brain activity using OEF-weighted imaging.[J].NeuroImage.2019,200:101-120.doi:10.1016/j.neuroimage.2019.06.038.
APA：
Yang Yang,Yayan Yin,Jie Lu,Qihong Zou&Jia-Hong Gao.(2019).Detecting resting-state brain activity using OEF-weighted imaging..NeuroImage,200,
MLA：
Yang Yang,et al."Detecting resting-state brain activity using OEF-weighted imaging.".NeuroImage 200.(2019):101-120
医学