He Yifang, Wang Nan, Liu Dongsheng, Peng Hao, Yin Shaoya, Wang Xiaosong, Wang Yong, Yang Yi, Si Juanning
Beijing Information Science and Technology University, School of Instrumentation Science and Opto-Electronics Engineering, Beijing, China.
Capital Medical University, Beijing Tiantan Hospital, Department of Neurosurgery, Beijing, China.
Neurophotonics. 2024 Oct;11(4):045013. doi: 10.1117/1.NPh.11.4.045013. Epub 2024 Dec 12.
The accurate assessment and classification of residual consciousness are crucial for optimizing therapeutic interventions in patients with disorders of consciousness (DOCs). However, there remains an absence of effective and definitive diagnostic methods for DOC in clinical practice.
The primary objective was to investigate the feasibility of utilizing resting state functional near-infrared spectroscopy (rs-fNIRS) for evaluating residual consciousness. The secondary objective was to explore the distinguishing characteristics that are more effective in differentiating between the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) and to identify the machine learning model that offers superior classification accuracy.
We utilized rs-fNIRS to evaluate the residual consciousness in patients with DOC. Specifically, rs-fNIRS was used to construct functional brain networks, and graph theory analysis was conducted to quantify the topological differences within these brain networks between MCS and UWS. After that, two classifiers were used to distinguish MCS from UWS.
The graph theory results showed that the MCS group ( ) exhibited significantly higher global efficiency ( ) and smaller characteristic path length ( ) than the UWS group ( ). The functional connectivity results showed that the correlation within the left occipital cortex (L_OC) was significantly lower in the MCS group than in the UWS group. By using the indicators with significant differences as features for further classification, the accuracy for -nearest neighbors and linear discriminant analysis classifiers was improved by 0.89 and 0.83, respectively.
The resting state functional connectivity and graph theory analysis based on fNIRS has the potential to enhance the classification accuracy, providing valuable insights into the diagnosis of patients with DOC.
对意识障碍（DOC）患者的残余意识进行准确评估和分类，对于优化治疗干预措施至关重要。然而，临床实践中仍缺乏针对DOC的有效且明确的诊断方法。
主要目的是研究利用静息态功能近红外光谱（rs-fNIRS）评估残余意识的可行性。次要目的是探索在区分无反应觉醒综合征（UWS）和最低意识状态（MCS）方面更有效的鉴别特征，并确定具有更高分类准确性的机器学习模型。
我们利用rs-fNIRS评估DOC患者的残余意识。具体而言，rs-fNIRS用于构建功能性脑网络，并进行图论分析以量化MCS和UWS之间这些脑网络内的拓扑差异。之后，使用两种分类器区分MCS和UWS。
图论结果表明，MCS组（ ）的全局效率（ ）显著高于UWS组（ ），特征路径长度（ ）显著小于UWS组。功能连接结果表明，MCS组左枕叶皮质（L_OC）内的相关性显著低于UWS组。通过将具有显著差异的指标用作进一步分类的特征， -最近邻和线性判别分析分类器的准确率分别提高了0.89和0.83。
基于fNIRS的静息态功能连接和图论分析有潜力提高分类准确性，为DOC患者的诊断提供有价值的见解。
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