Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, the Netherlands.
Donders Institute for Brain Cognition and Behaviour, Donders Center for Cognitive Neuroimaging, Radboud University, Kapittelweg 29, 6525EN Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Nobels Väg 9, D2:D235, 17177 Stockholm, Sweden.
Neuroimage. 2023 Apr 1;269:119880. doi: 10.1016/j.neuroimage.2023.119880. Epub 2023 Jan 21.
functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool to study cortical activity during movement and gait that requires further validation. This study aimed to assess (1) whether fNIRS can detect the difficult-to-measure leg area of the primary motor cortex (M1) and distinguish it from the hand area; and (2) whether fNIRS can differentiate between automatic (i.e., not requiring one's attention) and non-automatic movement processes. Special attention was attributed to systemic artifacts (i.e., changes in blood pressure, heart rate, breathing) which were assessed and corrected by short channels, i.e., fNIRS channels which are mainly sensitive to superficial scalp hemodynamics.
Twenty-three seated, healthy participants tapped four fingers on a keyboard or tapped the right foot on four squares on the floor in a specific order given by a 12-digit sequence (e.g., 434141243212). Two different sequences were executed: a beforehand learned (i.e., automatic) version and a newly learned (i.e., non-automatic) version. A 36-channel fNIRS device including 12 short channels covered multiple motor-related cortical areas including M1. The fNIRS data were analyzed with a general linear model (GLM). Correlation between the expected functional hemodynamic responses (i.e. task regressor) and the short channels (i.e. nuisance regressors), necessitated performing a separate short channel regression instead of integrating them in the GLM.
Consistent with the M1 somatotopy, we found significant HbO increases of very large effect size in the lateral M1 channels during finger tapping (Cohen's d = 1.35, p<0.001) and significant HbO increases of moderate effect size in the medial M1 channels during foot tapping (Cohen's d = 0.8, p<0.05). The cortical activity differences between automatic and non-automatic tasks were not significantly different. Importantly, leg movements produced large systemic fluctuations, which were adequately removed by the use of all available short channels.
Our results indicate that fNIRS is sensitive to leg activity in M1, though the sensitivity is lower than for finger activity and requires rigorous correction for systemic fluctuations. We furthermore highlight that systemic artifacts may result in an unreliable GLM analysis when short channels show signals that are similar to the expected hemodynamic responses.
功能近红外光谱（fNIRS）是一种越来越受欢迎的工具，用于研究运动和步态期间的皮质活动，需要进一步验证。本研究旨在评估：（1）fNIRS 是否可以检测到难以测量的运动皮质初级区（M1）的腿部区域，并将其与手部区域区分开来；（2）fNIRS 是否可以区分自动（即无需注意力）和非自动运动过程。特别注意系统伪影（即血压、心率、呼吸变化），通过短通道（即主要对头皮浅层血液动力学敏感的 fNIRS 通道）进行评估和校正。
23 名坐姿健康参与者在键盘上敲击四个手指或按照 12 位数字序列（例如 434141243212）敲击右脚四次。执行两个不同的序列：一个是事先学习的（即自动）版本，另一个是新学习的（即非自动）版本。一个包含 12 个短通道的 36 通道 fNIRS 设备覆盖了包括 M1 在内的多个与运动相关的皮质区域。使用广义线性模型（GLM）分析 fNIRS 数据。由于需要执行单独的短通道回归，而不是将它们集成到 GLM 中，因此在预期的功能血液动力学响应（即任务回归器）和短通道（即干扰回归器）之间进行相关性分析。
与 M1 躯体感觉图一致，我们发现手指敲击时外侧 M1 通道的 HbO 显著增加，具有非常大的效应量（Cohen's d = 1.35，p < 0.001），而脚部敲击时内侧 M1 通道的 HbO 显著增加，具有中等效应量（Cohen's d = 0.8，p < 0.05）。自动和非自动任务之间的皮质活动差异无显著差异。重要的是，腿部运动产生了较大的系统性波动，这些波动通过使用所有可用的短通道得到了充分消除。
我们的结果表明，fNIRS 对 M1 中的腿部活动敏感，尽管敏感性低于手指活动，并且需要对系统性波动进行严格校正。我们还强调，当短通道显示与预期血液动力学响应相似的信号时，系统性伪影可能导致不可靠的 GLM 分析。
