Artifacts in functional MRI (fMRI) data cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of the data -- all of which can have negative consequences for downstream statistical analysis. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts and generally comes in two flavors. Motion scrubbing based on subject head motion-derived measures is popular but suffers from a number of drawbacks, especially high rates of censoring of individual volumes and entire subjects. Alternatively, data-driven scrubbing methods like DVARS are based on observed noise in the processed fMRI timeseries and may avoid some of these issues. Here we propose "projection scrubbing", a novel data-driven scrubbing method based on a statistical outlier detection framework and strategic dimension reduction, including independent component analysis (ICA), to isolate artifactual variation. We undertake a comprehensive comparison of motion scrubbing with data-driven projection scrubbing and DVARS. We argue that an appropriate metric for the success of scrubbing is maximal data retention subject to reasonable performance on typical benchmarks of functional connectivity. We find that stringent motion scrubbing yields worsened validity, worsened reliability, and produced small improvements to fingerprinting. Meanwhile, data-driven scrubbing methods tend to yield greater improvements to fingerprinting while not generally worsening validity or reliability. Importantly, however, data-driven scrubbing excludes a fraction of the number of volumes or entire sessions compared to motion scrubbing. The ability of data-driven fMRI scrubbing to improve data retention without negatively impacting the quality of downstream analysis has major implications for sample sizes in population neuroscience research.

翻译：功能磁共振成像(fMRI)数据中的伪影会导致数据偏离常见分布假设，引入时空异常值，并降低信噪比——所有这些都会对下游统计分析产生负面影响。"擦洗"是一种排除被伪影污染的功能磁共振成像体素的技术，通常有两种形式。基于受试者头部运动指标的运动擦洗方法虽被广泛使用，但存在诸多缺陷，尤其是个体体素和整个受试者的高审查率。相比之下，基于处理后的fMRI时间序列中观测噪声的数据驱动擦洗方法（如DVARS）可规避部分问题。本文提出"投影擦洗"——一种基于统计异常值检测框架和策略性降维（包括独立成分分析ICA）的新型数据驱动擦洗方法，用于分离伪影变异。我们系统比较了运动擦洗与数据驱动投影擦洗及DVARS方法。我们认为，衡量擦洗效果的恰当指标应在功能连接典型基准测试中保持合理性能的前提下实现最大数据保留率。研究发现，严格的运动擦洗会降低效度和信度，仅对指纹识别产生微小改进；而数据驱动擦洗方法通常能显著提升指纹识别性能，且不损害效度或信度。值得关注的是，数据驱动擦洗排除的体素或完整扫描段数量远少于运动擦洗。这种在不影响下游分析质量前提下提升数据保留率的能力，对群体神经科学研究的样本量设计具有重要启示。