Delgado Reyes Lourdes M, Bohache Kevin, Wijeakumar Sobanawartiny, Spencer John P
University of East Anglia, School of Psychology, Norwich, United Kingdom.
University of Iowa, Iowa City, Iowa, United States.
Neurophotonics. 2018 Apr;5(2):025008. doi: 10.1117/1.NPh.5.2.025008. Epub 2018 May 22.
Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal components analysis (PCA), correlation-based signal improvement (CBSI), wavelet filtering, and spline interpolation. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Brigadoi et al. compared motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Given that fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. This study addresses that problem by evaluating motion correction algorithms implemented in HomER2. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response. Results showed that targeted PCA (tPCA), spline, and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using quantitative metrics. The CBSI method corrected many of the artifacts present in our data; however, this approach produced sometimes unstable HRFs. The targeted PCA and spline methods proved to be the most robust, performing well across all comparison metrics. When compared head to head, tPCA consistently outperformed spline. We conclude, therefore, that tPCA is an effective technique for correcting motion artifacts in fNIRS data from young children.
在功能近红外光谱（fNIRS）实验中，运动伪影通常是测量信号的一个重要组成部分。已经提出了多种方法来解决这个问题，包括主成分分析（PCA）、基于相关性的信号改进（CBSI）、小波滤波和样条插值。已经使用模拟数据比较了这些技术的功效；然而，我们对于这些技术在处理基于任务的认知数据时的表现了解有限。布里加多伊等人在一项简单认知任务期间测量的成人数据样本中比较了运动校正技术。小波滤波作为一种最佳的运动校正技术显示出最有前景。鉴于fNIRS经常用于婴儿和幼儿，直接用这些年龄组的数据评估运动校正技术的有效性至关重要。本研究通过评估在HomER2中实现的运动校正算法来解决这个问题。使用与血液动力学反应的生理特性相关的客观指标定量比较了每种技术的功效。结果表明，靶向主成分分析（tPCA）、样条和CBSI保留的试验次数更多。这些技术在使用定量指标与其他方法的直接对比中也表现良好。CBSI方法校正了我们数据中存在的许多伪影；然而，这种方法有时会产生不稳定的血流动力学反应函数（HRF）。靶向主成分分析和样条方法被证明是最稳健的，在所有比较指标上都表现良好。当直接比较时，tPCA始终优于样条。因此，我们得出结论，tPCA是校正幼儿fNIRS数据中运动伪影的一种有效技术。
