11 - A robust outlier detection approach for scrubbing artifacts in fMRI

Functional magnetic resonance imaging (fMRI) data can be artificially contaminated due to both participant and hardware-related reasons. In fMRI-based studies, it is, therefore, ¬-necessary to identify artifactual volumes. These are often excluded from analysis, a procedure known as "scrubbing" or "censoring". Such volumes contain abnormal signal intensities and can be thought of as multivariate outliers in statistical terminology. There exist many outlier-detection approaches for multivariate data and for fMRI data specifically. However, these methods either are non-robust or do not use a statistically principled approach to thresholding. Robust distance (RD) approaches that are adopted from Mahalanobis distance are promising but depend on assumptions of Gaussianity and independence, which we observe to be clearly violated in the fMRI context. When these assumptions are violated, the distribution of these RDs is unknown, preventing us from obtaining a quantile-based threshold for outliers. In this work, we develop a robust nonparametric bootstrap procedure to estimate an upper quantile of the distribution of RDs, which serves as the threshold for outliers. We compare the performance of our RD-based approach with existing "scrubbing" approaches for fMRI data employing 5 resting-state fMRI sessions with high levels of artifacts from the Human Connectome Project.

neuroimaging

functional MRI

outlier

bootstrap

robust 

Mid-Level

Knowledge

Women in Statistics and Data Science 2022