The Mind Research Network, Albuquerque, New Mexico, USA.
Magn Reson Imaging. 2013 Jun;31(5):707-17. doi: 10.1016/j.mri.2012.11.007. Epub 2013 Jan 3.
Motion correction is an important step in the functional magnetic resonance imaging (fMRI) analysis pipeline. While many studies simply exclude subjects who are estimated to have moved beyond an arbitrary threshold, there exists no objective method for determining an appropriate threshold. Furthermore, any criterion based only upon motion estimation ignores the potential for proper realignment. The method proposed here uses unsupervised learning (specifically k-means clustering) on features derived from the mean square derivative (MSD) of the signal before and after realignment to identify problem data. These classifications are refined through analysis of correlation between subject activation maps and the mean activation map, as well as the relationship between tasking and motion as measured through regression of the canonical hemodynamic response functions to fit both estimated motion parameters and MSD. The MSD is further used to identify specific scans containing residual motion, data which is suppressed by adding nuisance regressors to the general linear model; this statistical suppression is performed for identified problem subjects, but has potential for use over all subjects. For problem subjects, our results show increased hemodynamic activity more consistent with group results; that is, the addition of nuisance regressors resulted in a doubling of the correlation between the activation map for the problem subjects and the activation map for all subjects. The proposed method should be useful in helping fMRI researchers make more efficient use of their data by reducing the need to exclude entire subjects from studies and thus collect new data to replace excluded subjects.
运动校正（Motion correction）是功能磁共振成像（fMRI）分析流程中的一个重要步骤。虽然许多研究只是简单地排除那些被估计超出任意阈值的受试者，但目前还没有确定适当阈值的客观方法。此外，任何仅基于运动估计的标准都忽略了正确重新对齐的可能性。这里提出的方法使用无监督学习（特别是 k-均值聚类）对信号在重新对齐前后的均方导数（Mean Square Derivative，MSD）所提取的特征进行分类，以识别有问题的数据。通过分析受试者激活图与平均激活图之间的相关性，以及任务与运动之间的关系（通过对标准血流动力学响应函数进行回归来拟合估计的运动参数和 MSD）来对这些分类进行细化。MSD 进一步用于识别包含残留运动的特定扫描，这些数据是通过向一般线性模型添加混杂回归来抑制的；对于识别出的有问题的受试者，会对其进行这种统计抑制，但也有可能对所有受试者使用。对于有问题的受试者，我们的结果显示其血液动力学活动增加，与组结果更加一致；也就是说，添加混杂回归会导致问题受试者的激活图与所有受试者的激活图之间的相关性增加一倍。该方法应该有助于 fMRI 研究人员更有效地利用他们的数据，减少因排除整个受试者而需要从研究中排除整个受试者的情况，并因此收集新的数据来替代被排除的受试者。
