Study of functional connectivity during anesthesia based on sparse partial least squares

麻醉意识状态监测是神经科学基础研究及临床应用中的重要问题，受到广泛关注。本研究为寻找临床麻醉意识状态监测指标，共采集 14 位全麻手术患者在三种意识状态（清醒、中度麻醉、深度麻醉）下各 5 min 静息态脑电数据，对比采用稀疏偏最小二乘（SPLS）和传统的同步似然（SL）方法计算脑功能连接，通过连接特征来区分麻醉前后三种意识状态。通过全脑网络分析，本文 SPLS 方法与传统 SL 方法得到的不同意识状态下的网络参数变化趋势一致，并且采用 SPLS 方法所得结果的差异具有统计学意义（ P<0.05）。对 SPLS 方法得到的连接特征运用支持向量机进行分类，分类准确率为 87.93%，较使用 SL 方法得到的连接特征分类准确率高出 7.69%。本文研究结果显示，基于 SPLS 方法进行功能连接分析在区分三种意识状态方面有更好的性能，或可为临床麻醉监测提供一种新思路。.; Anesthesia consciousness monitoring is an important issue in basic neuroscience and clinical applications, which has received extensive attention. In this study, in order to find the indicators for monitoring the state of clinical anesthesia, a total of 14 patients undergoing general anesthesia were collected for 5 minutes resting electroencephalogram data under three states of consciousness (awake, moderate and deep anesthesia). Sparse partial least squares (SPLS) and traditional synchronized likelihood (SL) are used to calculate brain functional connectivity, and the three conscious states before and after anesthesia were distinguished by the connection features. The results show that through the whole brain network analysis, SPLS and traditional SL method have the same trend of network parameters in different states of consciousness, and the results obtained by SPLS method are statistically significant ( P<0.05). The connection features obtained by the SPLS method are classified by the support vector machine, and the classification accuracy is 87.93%, which is 7.69% higher than that of the connection feature classification obtained by SL method. The results of this study show that the functional connectivity based on the SPLS method has better performance in distinguishing three kinds of consciousness states, and may provides a new idea for clinical anesthesia monitoring.