Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Department of Neurology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Brain Res. 2023 Jan 1;1798:148162. doi: 10.1016/j.brainres.2022.148162. Epub 2022 Nov 11.
Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, P BA10 = 0.01, P BA10 < 0.01; nodal efficiency, P BA10 = 0.03, P BA10 < 0.01). Compared to healthy controls, UWS had impaired functions in both short- and long-distance connectivity, however, MCS had significantly impaired functions only in long-distance connectivity. The functional connectivity of right BA10 (AUC = 0.88) and the connections between left BA46 and right BA10 (AUC = 0.86) had excellent performance in differentiating MCS and UWS. MCS and UWS have different patterns of topological architecture and short- and long-distance connectivity in PFC. Intraconnections within BA10 and interhemispheric connections between BA10 and 46 are excellent resting-state fNIRS classifiers for distinguishing between MCS and UWS.
功能近红外光谱（fNIRS）是一种新兴的非侵入性技术，可在床边测量血氧水平依赖的血液动力学信号。我们旨在研究静息状态 fNIRS 在检测意识障碍（DOC）患者残留功能网络中的有效性。我们对 23 名 DOC 患者进行了静息状态 fNIRS 检查，其中 12 名患者处于最小意识状态（MCS），11 名患者处于无反应性觉醒状态（UWS）。选择前额叶皮层（PFC）的 10 个感兴趣区域（ROI）：双侧布罗德曼区（BA）9、BA10、BA44、BA45 和 BA46。使用图论分析和基于种子的相关分析来研究网络拓扑结构和 ROI 与通道之间的成对连接强度。MCS 和 UWS 表现出不同程度的拓扑结构丧失，BA10 的区域节点属性在两者之间存在显著差异（节点度，PBA10=0.01，PBA10<0.01；节点效率，PBA10=0.03，PBA10<0.01）。与健康对照组相比，UWS 在短程和远程连接中均存在功能障碍，而 MCS 仅在远程连接中存在明显的功能障碍。右侧 BA10 的功能连接（AUC=0.88）和左侧 BA46 与右侧 BA10 之间的连接（AUC=0.86）在区分 MCS 和 UWS 方面具有出色的性能。MCS 和 UWS 在 PFC 中具有不同的拓扑结构和短程与远程连接模式。BA10 内的内连接和 BA10 与 46 之间的半球间连接是区分 MCS 和 UWS 的出色静息状态 fNIRS 分类器。
