Study of functional connectivity during anesthesia based on sparse partial least squares
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摘要: 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。Abstract: 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.
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图 6 不同脑区平均连接值（*P < 0.05）
Figure 6. Average connectivity value of different brain regions（*P < 0.05）
表 1 效应室浓度
Table 1. Effect-site concentration
意识状态 效应室浓度/（μg﹒mL−1） 开始 结束 清醒 0 0 中度麻醉 1.2 1.2 深度麻醉 4.0 4.1 
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表 2 网络参数
Table 2. Network parameters
意识状态 SL SPLS C L C L 清醒 0.780 ± 0.053 1.338 ± 0.096 0.531 ± 0.037 5.292 ± 0.921 中度麻醉 0.734 ± 0.092 1.468 ± 0.161 0.529 ± 0.046 5.826 ± 0.612 深度麻醉 0.697 ± 0.028 1.514 ± 0.071 0.492 ± 0.068 6.324 ± 0.797 P 值 0.377 0.102 0.032 0.008
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表 3 分类结果比较
Table 3. Comparison of classification results
功能连接计算方法 主成分个数 分类准确率 SL 21 80.24% SPLS 25 87.93%
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[1] Cascella M, Schiavone V, Muzio M R, et al. Consciousness fluctuation during general anesthesia: a theoretical approach to anesthesia awareness and memory modulation. Curr Med Res Opin, 2016, 32(8): 1351-1359. doi: 10.1080/03007995.2016.1174679 [2] Shanks A M, Avidan M S, Kheterpal S, et al. Alerting thresholds for the prevention of intraoperative awareness with explicit recall: a secondary analysis of the michigan awareness control study. Eur J Anaesthesiol, 2015, 32(5): 346-353. doi: 10.1097/EJA.0000000000000123 [3] Matsushita S, Oda S, Otaki K, et al. Change in auditory evoked potential index and bispectral index during induction of anesthesia with anesthetic drugs. J Clin Monit Comput, 2015, 29(5): 621-626. doi: 10.1007/s10877-014-9643-x [4] Punjasawadwong Y, Chau-In W, Laopaiboon M, et al. Processed electroencephalogram and evoked potential techniques for amelioration of postoperative delirium and cognitive dysfunction following non-cardiac and non-neurosurgical procedures in adults. Cochrane Database of Systematic Reviews, 2018(5): 1-58. [5] Purdon P L, Sampson A, Pavone K J, et al. Clinical electroencephalography for anesthesiologists part I: background and basic signatures. Anesthesiology, 2015, 123(4): 937-960. doi: 10.1097/ALN.0000000000000841 [6] 康芳, 方才. BIS、AEP 用于全身麻醉深度监测临床价值评估. 安徽医学, 2007, 28(3): 275-277. doi: 10.3969/j.issn.1000-0399.2007.03.051 [7] 王之遥, 张军. 意识与脑功能连接性: 麻醉药物作用机制的研究进展. 国际麻醉学与复苏杂志, 2012, 33(10): 696-700. doi: 10.3760/cma.j.issn.1673-4378.2012.10.011 [8] Long Jinyi, Xie Qiuyou, Ma Qing, et al. Distinct interactions between fronto-parietal and default mode networks in impaired consciousness. Sci Rep, 2016, 6: 38866. doi: 10.1038/srep38866 [9] Allen E A, Damaraju E, Eichele T, et al. EEG signatures of dynamic functional network connectivity states. Brain Topogr, 2018, 31(1): 101-116. doi: 10.1007/s10548-017-0546-2 [10] Fraga González G, Smit D J A, van der Molen M J W, et al. EEG resting state functional connectivity in adult dyslexics using phase lag index and graph analysis. Front Hum Neurosci, 2018, 12: 341. [11] Rosales F, García-Dopico A, Bajo R, et al. An efficient implementation of the synchronization likelihood algorithm for functional connectivity. Neuroinformatics, 2015, 13(2): 245-258. doi: 10.1007/s12021-014-9251-4 [12] Wong K K K. Mediation analysis, categorical moderation analysis, and higher-order constructs modeling in partial least squares structural equation modeling (PLS-SEM): A B2B example using SmartPLS. Marketing Bulletin, 2016, 26: 1-22. [13] Lee D, Lee W, Lee Y, et al. Sparse partial least-squares regression and its applications to high-throughput data analysis. Chemometrics and Intelligent Laboratory Systems, 2011, 109(1): 1-8. doi: 10.1016/j.chemolab.2011.07.002 [14] Monteiro J M, Rao A, Shawe-Taylor J, et al. A multiple hold-out framework for sparse partial least squares. J Neurosci Methods, 2016, 271: 182-194. doi: 10.1016/j.jneumeth.2016.06.011 -
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