Curtis Andrew T, Hutchison R Matthew, Menon Ravi S
Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, 100 Perth Drive, London, Ontario, N6A 5K8, Canada; Department of Medical Biophysics, The University of Western Ontario, London, Ontario, N6A 5C1, Canada.
Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, 100 Perth Drive, London, Ontario, N6A 5K8, Canada; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Neuroimage. 2014 Oct 15;100:51-9. doi: 10.1016/j.neuroimage.2014.05.079. Epub 2014 Jun 5.
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.
静息态功能磁共振成像（RS-fMRI）是一种广泛用于推断脑区或节点之间连通性的方法。与基于任务的功能磁共振成像一样，连通性图谱的空间特异性可能会因宏观静脉中血氧水平依赖（BOLD）信号的强烈偏倚效应而失真。在RS-fMRI中，这种效应会因大脑中广泛分布的大静脉之间生理起源的时间相干性而加剧。在绝大多数RS-fMRI所使用的基于梯度回波的回波平面成像（EPI）中，携带与BOLD相关变化的宏观静脉表现出强烈的相位响应。这使得可以使用相位回归技术对静脉信号进行后处理识别和去除。在此，我们采用这种方法来抑制高场全脑RS-fMRI数据集中的大血管静脉贡献，从而导致网络的空间定位以及网络节点之间的相关性都发生显著变化。在个体和组分析层面均观察到了这些效应，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研究中的一个混杂因素。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。
