Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning
Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain n...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:e35e10e114bd49b58cc8f99b3589b3fa2021-11-12T06:53:53ZPredicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning1663-436510.3389/fnagi.2021.715517https://doaj.org/article/e35e10e114bd49b58cc8f99b3589b3fa2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnagi.2021.715517/fullhttps://doaj.org/toc/1663-4365Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR.Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).Zhaoshun JiangZhaoshun JiangYuxi CaiYuxi CaiXixue ZhangXixue ZhangYating LvMengting ZhangMengting ZhangShihong LiGuangwu LinZhijun BaoZhijun BaoZhijun BaoSongbin LiuSongbin LiuWeidong GuWeidong GuFrontiers Media S.A.articledelayed neurocognitive recoverydefault mode networkfunctional connectivitymachine learningresting-state functional MRINeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Aging Neuroscience, Vol 13 (2021) |
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| topic |
delayed neurocognitive recovery default mode network functional connectivity machine learning resting-state functional MRI Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
| spellingShingle |
delayed neurocognitive recovery default mode network functional connectivity machine learning resting-state functional MRI Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Zhaoshun Jiang Zhaoshun Jiang Yuxi Cai Yuxi Cai Xixue Zhang Xixue Zhang Yating Lv Mengting Zhang Mengting Zhang Shihong Li Guangwu Lin Zhijun Bao Zhijun Bao Zhijun Bao Songbin Liu Songbin Liu Weidong Gu Weidong Gu Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning |
| description |
Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR.Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096). |
| format |
article |
| author |
Zhaoshun Jiang Zhaoshun Jiang Yuxi Cai Yuxi Cai Xixue Zhang Xixue Zhang Yating Lv Mengting Zhang Mengting Zhang Shihong Li Guangwu Lin Zhijun Bao Zhijun Bao Zhijun Bao Songbin Liu Songbin Liu Weidong Gu Weidong Gu |
| author_facet |
Zhaoshun Jiang Zhaoshun Jiang Yuxi Cai Yuxi Cai Xixue Zhang Xixue Zhang Yating Lv Mengting Zhang Mengting Zhang Shihong Li Guangwu Lin Zhijun Bao Zhijun Bao Zhijun Bao Songbin Liu Songbin Liu Weidong Gu Weidong Gu |
| author_sort |
Zhaoshun Jiang |
| title |
Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning |
| title_short |
Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning |
| title_full |
Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning |
| title_fullStr |
Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning |
| title_full_unstemmed |
Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning |
| title_sort |
predicting delayed neurocognitive recovery after non-cardiac surgery using resting-state brain network patterns combined with machine learning |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/e35e10e114bd49b58cc8f99b3589b3fa |
| work_keys_str_mv |
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