Study of functional connectivity during anesthesia based on sparse partial least squares_Journal of Biomedical Engineering

Authors：

WU Fan ^1,2 , JIANG Zhongyi ^1,2 , BI Hui ^1,2 , ZHANG Jun ³ , LI Shitong ⁴ ,  ZOU Ling ^1,2

1. School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R.China;
2. Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, Jiangsu 213164, P.R.China;
3. Department of Anesthesiology, Cancer Hospital of Fudan University, Shanghai 200032, P.R.China;
4. Department of Anesthesiology, Huashan Hospital of Fudan University, Shanghai 200040, P.R.China;

Corresponding author：

ZOU Ling, Email: zouling@cczu.edu.cn

Keywords：

state of consciousness during anesthesia; synchronization likelihood; sparse partial least squares; functional connectivity; network analysis

DOI：

10.7507/1001-5515.201904052

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： WU Fan, JIANG Zhongyi, BI Hui, ZHANG Jun, LI Shitong, ZOU Ling. Study of functional connectivity during anesthesia based on sparse partial least squares. Journal of Biomedical Engineering, 2020, 37(3): 419-426. doi: 10.7507/1001-5515.201904052 Copy

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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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