基于近红外光谱成像（NIRS）的静息态脑功能连接（RSFC）研究是一种新兴的脑功能整合研究方法。它凭借NIRS生态效度好、便宜便携、适用人群广等优点，已在婴幼儿发育研究和大样本临床应用研究中展现出巨大的优势。目前NIRS-RSFC研究均基于稳态假设，但是大量基于功能磁共振成像的研究已表明脑区间功能整合的实时波动不容忽视，从动态角度度量RSFC可以为脑功能整合提供进一步证据，对疾病诊断提供更敏感的指标。为此，本项目拟率先展开动态NIRS-RSFC研究。首先解决其中各类噪声对RSFC动态特征的干扰问题，并围绕RSFC微状态这一有意思的研究问题建立动态NIRS-RSFC分析方法流程，最后基于大样本的精神疾病数据集对所提出的分析方法进行可靠性验证。本项目将NIRS-RSFC从稳态推向动态，促进了该领域的发展。结合NIRS的优势，未来可能在儿童脑发育和临床疾病研究中发挥实际应用价值和临床指导意义。

Assessing resting-state functional connectivity (RSFC) by near-infrared spectroscopy (NIRS) is an emerging technology to investigate the functional integration of human brain. Taking advantage of portable, cost-effective, and suitable for everyone and everywhere features of NIRS, NIRS-RSFC has demonstrated a huge potential for brain development research of infants and clinical applications with large sample datasets. To date, all NIRS-RSFC studies are based on the steady-state assumption, but a lot of evidences from functional magnetic resonance imaging have shown that the real-time dynamics of RSFC are considerable. Assessing the dynamics of RSFC has potential to provide further evidence to investigate brain functional integration, and to find a more sensitive indicator for disease diagnosis. To this end, this project intends to investigate, for the first time, the dynamic behavior of NIRS-RSFC. Firstly, we will solve the dynamic interference of two kinds of NIRS noise. Then, we will purpose a framework processing to reveal hidden "microstate" pattern of NIRS-RSFC dynamics. Finally, the reliability of the framework processing will be verified in clinical applications of mental illness based on a large sample dataset. Compared with traditional steady-state analysis, dynamic analysis of NIRS-RSFC has potential to find more effective feature for disease diagnosis. Therefore, this project not only is innovative, but also has practical value and clinical significance.