Einalou Zahra, Maghooli Keivan, Setarehdan Seyaed Kamaledin, Akin Ata
Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Neurophotonics. 2017 Oct;4(4):041407. doi: 10.1117/1.NPh.4.4.041407. Epub 2017 Aug 21.
Functional near-infrared spectroscopy (fNIRS) has been proposed as an affordable, fast, and robust alternative to many neuroimaging modalities yet it still has long way to go to be adapted in the clinic. One request from the clinicians has been the delivery of a simple and straightforward metric (a so-called biomarker) from the vast amount of data a multichannel fNIRS system provides. We propose a simple-straightforward signal processing algorithm derived from [Formula: see text] data collected during a modified version of the color-word matching Stroop task that consists of three different conditions. The algorithm starts with a wavelet-transform-based preprocessing, then uses partial correlation analysis to compute the functional connectivity matrices at each condition and then computes the global efficiency values. To this end, a continuous wave 16 channels fNIRS device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in [Formula: see text] concentrations from 12 healthy volunteers. We have considered 10% of strongest connections in each network. A strong Stroop interference effect was found between the incongruent against neutral condition ([Formula: see text]) while a similar significance was observed for the global efficiency values decreased from neutral to congruent to incongruent conditions [[Formula: see text], [Formula: see text]]. The findings bring us closer to delivering a biomarker derived from fNIRS data that can be reliably and easily adopted by the clinicians.
功能近红外光谱技术（fNIRS）已被提议作为一种价格亲民、快速且可靠的替代方法，可用于多种神经成像方式，但要在临床上得到应用仍有很长的路要走。临床医生的一个要求是，从多通道fNIRS系统提供的大量数据中得出一个简单直接的指标（即所谓的生物标志物）。我们提出了一种简单直接的信号处理算法，该算法源自于在经过修改的颜色-单词匹配斯特鲁普任务的三个不同条件下收集的[公式：见正文]数据。该算法首先进行基于小波变换的预处理，然后使用偏相关分析来计算每个条件下的功能连接矩阵，接着计算全局效率值。为此，使用了一台连续波16通道fNIRS设备（ARGES Cerebro，Hemosoft公司，土耳其）来测量12名健康志愿者的[公式：见正文]浓度变化。我们考虑了每个网络中最强连接的10%。在不一致条件与中性条件之间发现了强烈的斯特鲁普干扰效应（[公式：见正文]），而对于从中性条件到一致条件再到不一致条件全局效率值下降的情况也观察到了类似的显著性（[公式：见正文]，[公式：见正文]）。这些发现使我们更接近提供一种源自fNIRS数据的生物标志物，该标志物能够被临床医生可靠且轻松地采用。
