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

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

JOURNAL=Frontiers in Neuroscience

VOLUME=15

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.707944

DOI=10.3389/fnins.2021.707944

ISSN=1662-453X

ABSTRACT=<p><bold>Objectives:</bold> 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.</p><p><bold>Method:</bold> 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.</p><p><bold>Results:</bold> Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the <italic>independent-sample T test</italic> between these features and the categories, 15 features showed statistical differences (<italic>P</italic> &lt; 0.05) and 3 features had significant statistical differences (<italic>P</italic> &lt; 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.</p><p><bold>Conclusion:</bold> 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.</p>