Yuan Lisha, He Hongjian, Zhang Han, Zhong Jianhui
Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University Hangzhou, China.
Center for Cognition and Brain Disorders, Hangzhou Normal UniversityHangzhou, China; Department of Radiology and BRIC, University of North Carolina at Chapel HillChapel Hill, NC, USA.
Front Neurosci. 2016 Dec 27;10:591. doi: 10.3389/fnins.2016.00591. eCollection 2016.
Head motion is one of major concerns in current resting-state functional MRI studies. Image realignment including motion estimation and spatial resampling is often applied to achieve rigid-body motion correction. While the accurate estimation of motion parameters has been addressed in most studies, spatial resampling could also produce spurious variance, and lead to unexpected errors on the amplitude of BOLD signal. In this study, two simulation experiments were designed to characterize these variance related with spatial resampling. The fluctuation amplitude of spurious variance was first investigated using a set of simulated images with estimated motion parameters from a real dataset, and regions more likely to be affected by spatial resampling were found around the peripheral regions of the cortex. The other simulation was designed with three typical types of motion parameters to represent different extents of motion. It was found that areas with significant correlation between spurious variance and head motion scattered all over the brain and varied greatly from one motion type to another. In the last part of this study, four popular motion regression approaches were applied respectively and their performance in reducing spurious variance was compared. Among them, Friston 24 and Voxel-specific 12 model (Friston et al., 1996), were found to have the best outcomes. By separating related effects during fMRI analysis, this study provides a better understanding of the characteristics of spatial resampling and the interpretation of motion-BOLD relationship.
头部运动是当前静息态功能磁共振成像（fMRI）研究中的主要关注点之一。包括运动估计和空间重采样在内的图像重新对齐通常用于实现刚体运动校正。虽然大多数研究都讨论了运动参数的准确估计，但空间重采样也可能产生伪方差，并导致血氧水平依赖（BOLD）信号幅度出现意外误差。在本研究中，设计了两个模拟实验来表征与空间重采样相关的这些方差。首先使用一组具有来自真实数据集估计运动参数的模拟图像研究了伪方差的波动幅度，并发现皮质周边区域周围更可能受到空间重采样影响的区域。另一个模拟实验设计了三种典型的运动参数类型来代表不同程度的运动。结果发现，伪方差与头部运动之间具有显著相关性的区域散布在整个大脑中，并且在不同的运动类型之间差异很大。在本研究的最后一部分，分别应用了四种常用的运动回归方法，并比较了它们在减少伪方差方面的性能。其中，Friston 24和体素特异性12模型（Friston等人，1996年）被发现具有最佳效果。通过在功能磁共振成像分析过程中分离相关效应，本研究更好地理解了空间重采样的特征以及运动与BOLD关系的解释。
