Multi-Order Brain Functional Connectivity Network-Based Machine Learning Method for Recognition of Delayed Neurocognitive Recovery in Older Adults Undergoing Non-cardiac Surgery

Guoqing Wu; Zhaoshun Jiang; Yuxi Cai; Xixue Zhang; Yating Lv; Shihong Li; Guangwu Lin; Zhijun Bao; Songbin Liu; Weidong Gu

doi:10.3389/fnins.2021.707944

Multi-Order Brain Functional Connectivity Network-Based Machine Learning Method for Recognition of Delayed Neurocognitive Recovery in Older Adults Undergoing Non-cardiac Surgery

Front Neurosci. 2021 Sep 16:15:707944. doi: 10.3389/fnins.2021.707944. eCollection 2021.

Authors

Guoqing Wu¹, Zhaoshun Jiang², Yuxi Cai², Xixue Zhang², Yating Lv³, Shihong Li⁴, Guangwu Lin⁴, Zhijun Bao⁵, Songbin Liu², Weidong Gu²

Affiliations

¹ School of Information Science and Technology, Fudan University, Shanghai, China.
² Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
³ Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.
⁴ Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
⁵ Department of Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, China.

Abstract

Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR. Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model. Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients' brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively. Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.

Keywords: delayed neurocognitive recovery; functional connectivity; machine learning; resting-state functional magnetic resonance imaging; sparse representation.