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Jiang Z, Cai Y, Liu S, Ye P, Yang Y, Lin G, Li S, Xu Y, Zheng Y, Bao Z, Nie S, Gu W. Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis. Front Aging Neurosci 2023; 14:1109485. [PMID: 36688167 PMCID: PMC9853194 DOI: 10.3389/fnagi.2022.1109485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/16/2022] [Indexed: 01/07/2023] Open
Abstract
Objectives The abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms. Methods Resting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test. Results We found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus. Conclusion The decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR. Clinical Trial Registration : Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.
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Affiliation(s)
- Zhaoshun Jiang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yuxi Cai
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Songbin Liu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Pei Ye
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yifeng Yang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yan Xu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yangjing Zheng
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhijun Bao
- Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Department of Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Research Center on Aging and Medicine, Fudan University, Shanghai, China
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China,Shengdong Nie,
| | - Weidong Gu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China,*Correspondence: Weidong Gu,
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