Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, USA.
Department of Psychology, Georgetown University, Washington, DC, USA.
Neuroimage. 2019 Jan 1;184:171-179. doi: 10.1016/j.neuroimage.2018.09.025. Epub 2018 Sep 11.
Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging technique of growing interest as a tool for investigation of cortical activity. Due to the on-head placement of optodes, artifacts arising from head motion are relatively less severe than for functional magnetic resonance imaging (fMRI). However, it is still necessary to remove motion artifacts. We present a novel motion correction procedure based on robust regression, which effectively removes baseline shift and spike artifacts without the need for any user-supplied parameters. Our simulations show that this method yields better activation detection performance than 5 other current motion correction methods. In our empirical validation on a working memory task in a sample of children 7-15 years, our method produced stronger and more extensive activation than any of the other methods tested. The new motion correction method enhances the viability of fNIRS as a functional neuroimaging modality for use in populations not amenable to fMRI.
功能性近红外光谱（fNIRS）是一种日益受到关注的光学神经影像学技术，可作为研究皮质活动的工具。由于光极放置在头部，因此与功能磁共振成像（fMRI）相比，源自头部运动的伪影相对不那么严重。然而，仍然需要去除运动伪影。我们提出了一种基于稳健回归的新运动校正程序，该程序可有效地去除基线漂移和尖峰伪影，而无需任何用户提供的参数。我们的模拟表明，与其他 5 种当前的运动校正方法相比，该方法具有更好的激活检测性能。在我们对 7-15 岁儿童的工作记忆任务的实证验证中，与测试的任何其他方法相比，我们的方法产生了更强和更广泛的激活。新的运动校正方法提高了 fNIRS 作为一种适用于不能进行 fMRI 的人群的功能神经影像学模态的可行性。
