Significance: There is an emerging need for convenient and continuous bedside monitoring of full-term newborns with hypoxic-ischemic brain damage (HIBD) to determine whether early intervention is required. Functional near-infrared spectroscopy (fNIRS)-based resting-state brain network analysis, which could provide an effective evaluation method, remains to be extensively studied. Aim: Our study aims to verify the feasibility of fNIRS-based resting-state brain networks for evaluating brain function in infants with HIBD to provide a new and effective means for clinical research in neonatal HIBD. Approach: Thirteen neonates with HIBD were scanned using fNIRS in the resting state. The brain network properties were explored to attempt to extract effective features as recognition indicators. Results: Compared with healthy controls, newborns with HIBD showed decreased brain functional connectivity. Specifically, there were severe losses of long-range functional connectivity of the contralateral parietal-temporal lobe, contralateral parietal-frontal lobe, and contralateral parietal lobe. The node degree showed a widespread decrease in the left frontal middle gyrus, left superior frontal gyrus dorsal, and right central posterior gyrus. However, newborns with HIBD showed a significantly higher local network efficiency (*p < 0.05). Subsequently, network indicators based on small-worldness, local efficiency, modularity, and normalized clustering coefficient were extracted for HIBD identification with the accuracy observed as 79.17%. Conclusions: Our findings indicate that fNIRS-based resting-state brain network analysis could support early HIBD diagnosis.

中文翻译：

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新生儿缺氧缺血性脑损伤中的静息状态脑网络：功能性近红外光谱研究

启示：迫切需要对缺氧缺血性脑损伤（HIBD）足月新生儿进行连续，连续的床旁监测，以确定是否需要早期干预。基于功能近红外光谱（fNIRS）的静止状态脑网络分析可以提供一种有效的评估方法，仍有待广泛研究。目的：我们的研究旨在验证基于fNIRS的静止状态大脑网络评估HIBD婴儿脑功能的可行性，从而为新生儿HIBD的临床研究提供一种新的有效手段。方法：在静止状态下使用fNIRS对13名HIBD新生儿进行了扫描。探索了大脑网络的属性，以尝试提取有效的特征作为识别指标。结果：与健康对照组相比，患有HIBD的新生儿表现出大脑功能连接性下降。特别是，对侧顶颞叶，对侧顶额叶和对侧顶叶的远距离功能连通性严重丧失。结节度显示左额中回，左额上回，中央右后回广泛减少。但是，患有HIBD的新生儿表现出明显更高的局域网效率（* p <0.05）。随后，基于小世界性，局部效率，模块性和归一化聚类系数的网络指标被提取用于HIBD识别，观察到的准确性为79.17％。结论：我们的发现表明基于fNIRS的静息状态脑网络分析可以支持HIBD的早期诊断。