Detecting resting-state brain activity using OEF-weighted imaging
Detecting resting-state brain activity using OEF-weighted imaging
复制标题
使用 OEF 加权成像检测静息态大脑活动
DOI：
10.1016/j.neuroimage.2019.06.038
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发表时间：
2019-10
期刊：
影响因子：
5.7
通讯作者：
Gao J. H.
中科院分区：
文献类型：
--
作者：
Yang Y;Yin Y;Lu J;Zou Q;Gao J. H.
传统的静息态功能磁共振成像（fMRI）主要基于血氧水平依赖（BOLD）对比。氧提取分数（OEF）代表脑代谢的重要参数，并且是组织活力的关键生物标志物，检测氧利用与氧递送的比率。研究OEF加权信号的自发波动对于理解大脑活动的潜在机制至关重要，因为在静息状态下存在巨大的能量预算。然而，由于OEF映射的时间分辨率差，没有研究报告使用OEF对比度来评估静息态脑活动。在这项fMRI研究中，我们记录了健康志愿者在两次扫描访问中的大脑OEF加权波动10分钟，使用我们最近开发的脉冲序列，可以获得全脑体素明智的OEF加权信号，时间分辨率为3秒。使用组独立成分分析和基于种子的功能连接分析，我们鲁棒地确定了内在的大脑网络，包括内侧视觉，外侧视觉，听觉，默认模式和双边执行控制网络，使用OEF对比。此外，我们研究了休息状态的局部特征的脑活动的基础上，使用区域均匀性（ReHo）和分数振幅的低频波动（fALFF）的OEF加权信号。我们证明，大脑的灰质区域，特别是那些在默认模式网络，显示出较高的ReHo和fALFF值与OEF对比度。此外，在整个大脑的体素明智的重测信度比较表明，休息状态下的脑活动的OEF对比度的基础上的可靠性是中等的网络指数和高的局部活动指数，特别是ReHo。虽然基于OEF的指标的信度普遍低于基于BOLD的指标，但OEF-ReHo的信度略高于BOLD-ReHo，且效应量较小，表明OEF-ReHo可以作为BOLD的补充，作为表征静息状态局部脑活动的可靠指标。因此，OEF可以作为研究静息态脑活动的有效对比，具有中等到较高的重测信度。
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.
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