Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis
Abstract Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are as...
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| Format: | Article |
| Language: | English |
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BMC
2021-09-01
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| Series: | Translational Neurodegeneration |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40035-021-00255-0 |
| _version_ | 1818439432129216512 |
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| author | Wenbin Li Qianqian Wei Yanbing Hou Du Lei Yuan Ai Kun Qin Jing Yang Graham J. Kemp Huifang Shang Qiyong Gong |
| author_facet | Wenbin Li Qianqian Wei Yanbing Hou Du Lei Yuan Ai Kun Qin Jing Yang Graham J. Kemp Huifang Shang Qiyong Gong |
| author_sort | Wenbin Li |
| collection | DOAJ |
| description | Abstract Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. Methods Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. Results Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient C p (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. Conclusion Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice. |
| first_indexed | 2024-12-14T17:56:22Z |
| format | Article |
| id | doaj.art-a3a2acbe6ba749b69ecf0a680aa33a1b |
| institution | Directory Open Access Journal |
| issn | 2047-9158 |
| language | English |
| last_indexed | 2024-12-14T17:56:22Z |
| publishDate | 2021-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Translational Neurodegeneration |
| spelling | doaj.art-a3a2acbe6ba749b69ecf0a680aa33a1b2022-12-21T22:52:32ZengBMCTranslational Neurodegeneration2047-91582021-09-0110111210.1186/s40035-021-00255-0Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosisWenbin Li0Qianqian Wei1Yanbing Hou2Du Lei3Yuan Ai4Kun Qin5Jing Yang6Graham J. Kemp7Huifang Shang8Qiyong Gong9Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan UniversityLaboratory of Neurodegenerative Disorders, Departments of Neurology, West China Hospital of Sichuan UniversityLaboratory of Neurodegenerative Disorders, Departments of Neurology, West China Hospital of Sichuan UniversityDepartment of Psychiatry and Behavioral Neuroscience, Division of Bipolar Disorders Research, University of Cincinnati College of MedicineHuaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan UniversityHuaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan UniversityHuaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan UniversityDepartment of Musculoskeletal and Ageing Science and MRC - Versus Arthritis Centre for Integrated Research Into Musculoskeletal Ageing, Faculty of Health and Life Sciences, University of LiverpoolLaboratory of Neurodegenerative Disorders, Departments of Neurology, West China Hospital of Sichuan UniversityHuaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan UniversityAbstract Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. Methods Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. Results Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient C p (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. Conclusion Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.https://doi.org/10.1186/s40035-021-00255-0Amyotrophic lateral sclerosisWhite matterDTINetworkConnectomicsMachine learning |
| spellingShingle | Wenbin Li Qianqian Wei Yanbing Hou Du Lei Yuan Ai Kun Qin Jing Yang Graham J. Kemp Huifang Shang Qiyong Gong Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis Translational Neurodegeneration Amyotrophic lateral sclerosis White matter DTI Network Connectomics Machine learning |
| title | Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis |
| title_full | Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis |
| title_fullStr | Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis |
| title_full_unstemmed | Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis |
| title_short | Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis |
| title_sort | disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis |
| topic | Amyotrophic lateral sclerosis White matter DTI Network Connectomics Machine learning |
| url | https://doi.org/10.1186/s40035-021-00255-0 |
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