Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning
Background: and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not full...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024139217 |
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| author | Yuanyuan Wang Yu Ji Jie Liu Lianjiang Lv Zihe Xu Meimei Yan Jialu Chen Zhijun Luo Xianjun Zeng |
| author_facet | Yuanyuan Wang Yu Ji Jie Liu Lianjiang Lv Zihe Xu Meimei Yan Jialu Chen Zhijun Luo Xianjun Zeng |
| author_sort | Yuanyuan Wang |
| collection | DOAJ |
| description | Background: and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients. Methods: We recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. Results: All participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937. Conclusion: The alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs. |
| format | Article |
| id | doaj-art-f5444ec2c4b04066ac8e2dca6f1d4d08 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-f5444ec2c4b04066ac8e2dca6f1d4d082024-12-13T10:58:15ZengElsevierHeliyon2405-84402024-12-011023e37890Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learningYuanyuan Wang0Yu Ji1Jie Liu2Lianjiang Lv3Zihe Xu4Meimei Yan5Jialu Chen6Zhijun Luo7Xianjun Zeng8Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China; Corresponding author.Background: and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients. Methods: We recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. Results: All participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937. Conclusion: The alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.http://www.sciencedirect.com/science/article/pii/S2405844024139217Retinal detachmentDynamic functional connectivityGraph theory analysisBrain functional networkMachine learning |
| spellingShingle | Yuanyuan Wang Yu Ji Jie Liu Lianjiang Lv Zihe Xu Meimei Yan Jialu Chen Zhijun Luo Xianjun Zeng Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning Heliyon Retinal detachment Dynamic functional connectivity Graph theory analysis Brain functional network Machine learning |
| title | Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning |
| title_full | Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning |
| title_fullStr | Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning |
| title_full_unstemmed | Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning |
| title_short | Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning |
| title_sort | abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning |
| topic | Retinal detachment Dynamic functional connectivity Graph theory analysis Brain functional network Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024139217 |
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