近红外光谱脑功能成像技术（fNIRS）是一种非侵入的光学脑功能成像技术。近些年来已经被广泛的应用到探索各种各样的人类脑功能活动中。FNIRS具有安全，便携，低成本，和对运动相对不敏感的特点，使其能够应对特殊的，非实验室环境下的脑功能影像需求。例如，fNIRS能够适用于婴幼儿，病人，及老年人等无法进行功能磁共振（fMRI）扫描的特殊人群。同时，fNIRS特别适用于超扫描，即在自然场景中扫描多个正在交互的人脑，进而探索人与人交互的神经活动规律。最后，fNIRS可以简便和fMRI，EEG，TMS等神经影像设备联合使用，为更为全面，互补的方式获取人脑功能信息提供技术支持。
虽然fNIRS已经被广泛的应用，然而由于其特殊的技术特点，在进一步推广和应用上还面临挑战。本文基于fNIRS的技术特点，将fNIRS的现存问题分为两个方面：1.FNIRS的空间定位问题。由于fNIRS通过在受试者头颅放置光极来获取脑功能信息，故需要额外的方案来获取fNIRS的成像的脑结构信息。更能重要的是，fNIRS设备通常只提供有限对光极，故对于目标脑区如何规划和放置光极是一个挑战。2.FNIRS获取的时间序列中的噪声问题。由于fNIRS的成像原理的限制，其信号中通常含有各种类型的噪声，具有较低的信噪比，故如何对fNIRS获取到的原始信号去噪并得到有神经意义的成分问题。因此，本研究并分别在上述两个方面开展了三项工作来优化现有的fNIRS成像技术。
对于fNIRS的空间定位问题，课题组前期提出了经颅脑图谱（TBA）的概念，基于大样本结构磁共振像构建了高分辨率的概率颅脑映射，来帮助解决fNIRS的空间定位问题。然而，对于如何利用TBA对目标脑区规划和放置光极，前期工作主要依赖主试的直觉和操作，还没有相应的理论和实现方案。故在本研究中，我们提出了基于TBA的fNIRS靶向优化理论和实现方法。我们首先定义了fNIRS光极放置的两个目标：群体成像变异性（GIV）和群体成像准确性（GIA）。第二，我们提出了基于TBA的GIV和GIA的优化理论。第三，我们提出了两步流程来实现上述理论的应用：在第一步中，我们给出了在虚拟头模型上的自动光极排布算法产生最优的光极排布。在第二步中，我们阐明了利用三维头壳导航系统准确放置排布好的光极板的流程。通过真实fNIRS实验，我们验证了提出的方法，并与传统的基于10/20的方法做了比较。结果显示该方法在测量位置和激活的结果都优于传统的10/20方法。
对于fNIRS时间序列中的噪声问题，我们将独立成分分析（ICA）的方法更好的应用在了fNIRS的数据上。我们首先提出了基于MATLAB的NIRS-ICA工具包。NIRS-ICA集成了fNIRS领域广泛使用的ICA分解算法，并包含了定量的指标来评价ICA分解的结果来，从而帮助筛选噪声或神经活动相关的源。我们简单介绍了NIRS-ICA包含的算法，并用示例数据展示了NIRS-ICA的使用方法。第二，由于fNIRS非常适合用于超扫描的研究，我们为基于fNIRS的双人神经科学研究提出了基于ICA的源级别的脑间连接计算方法。我们首先将一对交互者的fNIRS数据分别用ICA分解成多个源，之后通过源的特征分别选择出感兴趣的源，并计算源的时间模式之间的相关性。我们用模拟实验和真实实验验证了提出的方法，并和传统通道级别的脑间连接计算方法做了比较，结果证明我们的方法比传统通道级别的脑间连接计算方法有更好的敏感性和特异性。

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical imaging technique that has been widely applied in the study of human brain functions. Being safe, portable, few in restrictions, and insensitive to body movements, fNIRS can be easily applied to special populations such as infants, patients, and the elderly. fNIRS offers high ecological validity and is suitable for conducting experiments in natural environments. Due to these advantages, fNIRS have provided new insights for social neuroscience via hyperscanning studies. It can also be easily combined with other neuroimaging techniques such as fMRI and EEG, to gain more comprehensive information of neural activity in the human brain.
Besides the advantages mentioned above, the fNIRS technique still faces challenges in application due to its imaging principle. In this thesis, the current challenges of the fNIRS technique are being summarized in two aspects. First, since fNIRS is a transcranial technique that can only obtain functional information of the human brain, it requires additional methods to locate the anatomical brain region imaged by fNIRS optodes. More importantly, fNIRS devices often provide only limited number of optodes, so how to arrange and place the optodes for imaging a target brain area is a challenge. We concluded these problems as the localization problem, which exists on the spatial dimension of fNIRS. Second, the signal of fNIRS is often contaminated by various types of noises, which can be considered as a problem in the temporal dimension. Three studies are carried out to address these two fundamental problems of the fNIRS technique in the current thesis.
For the spatial localization problem, our group previously proposed the transcranial brain atlases (TBAs), which provide high-resolution probabilistic scalp-brain correspondences based on large samples of individuals, to assist fNIRS optode arrangement and placement. However, due to a lack of theoretical basis, the optimization procedure of using TBAs remains intuition-based, subjective, and time-consuming. Therefore, in this study, we propose transcranial brain atlas-based targeting optimization for fNIRS. Specifically, we first describe the theoretical basis of the TBA-based optimization of optode arrangement using a mathematical framework. Second, based on the theoretical basis, an algorithm is proposed for automatically arranging optodes on a virtual scalp. The resultant montage is placed on the head of each participant guided by a low-cost and portable navigation system. We compared our method with the widely used 10/20-system-assisted optode arrangement procedure, using finger-tapping and working memory tasks as examples of both low- and high-level cognitive systems.
For the noises in the temporal dimension of fNIRS, we focus on independent component analysis, which is a multivariate data-driven approach whose promising effectiveness has been shown in both removing noise and extracting neural related components in fNIRS. We did two works to promote the ICA application in fNIRS. First, we propose NIRS-ICA, a MATLAB toolbox to ease the difficulty of ICA application for fNIRS studies. NIRS-ICA incorporates commonly used ICA algorithms for source separation, user-friendly GUI, and quantitative evaluation metrics assisting source selection, which facilitate both remove noise and extract sources of interest. The options used in the processing can also be reported easily. The algorithms involved in developing NIRS-ICA are introduced and the implementation and usages are demonstrated based on a representative fNIRS data set.
Second, since fNIRS is a very promising technique and produced valuable insights in two-person neuroscience by hyperscanning. Therefore, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.