Functional magnetic resonance imaging (fMRI) data contain high levels of noise and artifacts. To avoid contamination of downstream analyses, fMRI-based studies must identify and remove these noise sources prior to statistical analysis. One common approach is the "scrubbing" of fMRI volumes that are thought to contain high levels of noise. However, existing scrubbing techniques are based on ad hoc measures of signal change. We consider scrubbing via outlier detection, where volumes containing artifacts are considered multidimensional outliers. Robust multivariate outlier detection methods are proposed using robust distances (RDs), which are related to the Mahalanobis distance. These RDs have a known distribution when the data are i.i.d. normal, and that distribution can be used to determine a threshold for outliers where fMRI data violate these assumptions. Here, we develop a robust multivariate outlier detection method that is applicable to non-normal data. The objective is to obtain threshold values to flag outlying volumes based on their RDs. We propose two threshold candidates that embark on the same two steps, but the choice of which depends on a researcher's purpose. Our main steps are dimension reduction and selection, robust univariate outlier imputation to get rid of the effect of outliers on the distribution, and estimating an outlier threshold based on the upper quantile of the RD distribution without outliers. The first threshold candidate is an upper quantile of the empirical distribution of RDs obtained from the imputed data. The second threshold candidate calculates the upper quantile of the RD distribution that a nonparametric bootstrap uses to account for uncertainty in the empirical quantile. We compare our proposed fMRI scrubbing method to motion scrubbing, data-driven scrubbing, and restrictive parametric multivariate outlier detection methods.
翻译:功能磁共振成像(fMRI)数据中存在较高水平的噪声和人工伪像。为避免向下游分析传播这些噪声源,基于fMRI的研究必须在统计分析之前识别并清除这些噪声源。一种常见的方法是通过“清洗”被认为含有高水平噪声的fMRI体积。然而,现有的清洗技术基于信号变化的临时措施。我们考虑通过异常值检测进行清洗,其中被认为含有伪像的体积被视为多维异常值。我们提出了使用鲁棒性距离(RDs)的鲁棒性多元异常值检测方法,这些RDs与马氏距离有关。当数据满足i.i.d.正态分布时,这些RDs具有已知的分布,并且可以用于确定异常值的阈值,即fMRI数据违反这些假设。在这里,我们开发了一种适用于非正态数据的鲁棒性多元异常值检测方法。我们的目标是根据其RDs获得阈值来标记异常体积。我们提出了两个阈值候选项,这两个候选项依赖于研究人员的目的。我们的主要步骤是降维和选择,均值-方差鲁棒性单变量异常值插补以消除异常值对分布的影响,并根据没有异常值的RD分布的上分位数估计异常值阈值。第一个阈值候选项是从插补数据中获得的RD经验分布的上分位数。第二个阈值候选项计算了非参数自助法使用的RD分布的上分位数,以解决经验分位数中的不确定性。我们将我们提出的fMRI清洗方法与运动清洗,数据驱动清洗和受限参数多元异常值检测方法进行了比较。