Phase based venous suppression in resting-state BOLD GE-fMRI
Phase based venous suppression in resting-state BOLD GE-fMRI
复制标题
DOI：
10.1016/j.neuroimage.2014.05.079
复制
发表时间：
2014-10-15
期刊：
影响因子：
5.7
通讯作者：
Menon, Ravi S.
中科院分区：
文献类型：
--
作者：
Curtis, Andrew T.;Hutchison, R. Matthew;Menon, Ravi S.
静息态功能磁共振成像（RS-fMRI）是一种广泛使用的方法，用于推断大脑区域或节点之间的连接。与基于任务的功能磁共振成像一样，连接图的空间特异性可能会被宏观静脉中BOLD信号的强烈偏置效应所扭曲。在RS-fMRI中，这种效应因大脑中广泛分布的大静脉之间的生理起源的时间相干性而加剧。在用于绝大多数RS-fMRI的基于梯度回波的EPI中，携带BOLD相关变化的宏观静脉表现出强烈的相位响应。这允许使用相位回归器技术对静脉信号进行后处理识别和去除。在这里，我们采用这种方法来抑制大血管静脉的贡献，在高场全脑RS-fMRI数据集，导致显着的变化，网络的空间定位和网络节点之间的相关性。在个体和组分析水平上都观察到了这些影响，这表明即使在相对较低的图像分辨率下，静脉污染也是RS-fMRI研究的混杂因素。因此，使用相位回归方法抑制大血管信号可能有助于更好地识别、描绘和解释大规模脑网络的真实结构。(C)2014爱思唯尔公司All rights reserved.
Resting-state functional MRI (RS-fMRI) is a widely used method for inferring connectivity between brain regions or nodes. As with task-based fMRI, the spatial specificity of the connectivity maps can be distorted by the strong biasing effect of the BOLD signal in macroscopic veins. In RS-fMRI this effect is exacerbated by the temporal coherences of physiological origin between large veins that are widely distributed in the brain. In gradient echo based EPI, used for the vast majority of RS-fMRI, macroscopic veins that carry BOLD-related changes exhibit a strong phase response. This allows for post-processing identification and removal of venous signals using a phase regressor technique. Here, we employ this approach to suppress macrovascular venous contributions in high-field whole-brain RS-fMRI data sets, resulting in significant changes to both the spatial localization of the networks and the correlations between the network nodes. These effects were observed at both the individual and group analysis level, suggesting that venous contamination is a confounding factor for RS-fMRI studies even at relatively low image resolutions. Suppression of the macrovascular signal using the phase regression approach may therefore help to better identify, delineate, and interpret the true structure of large-scale brain networks. (C) 2014 Elsevier Inc. All rights reserved.