A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD
Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-worl...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2020/9436406 |
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| author | Hao Liu Haimeng Hu Huiying Wang Jiahui Han Yunfei Li Huihui Qi Meimei Wang Sisi Zhang Huijin He Xiaohu Zhao |
| author_facet | Hao Liu Haimeng Hu Huiying Wang Jiahui Han Yunfei Li Huihui Qi Meimei Wang Sisi Zhang Huijin He Xiaohu Zhao |
| author_sort | Hao Liu |
| collection | DOAJ |
| description | Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD. |
| format | Article |
| id | doaj-art-58e4c0efd07a4bb79f494539371cd22d |
| institution | OA Journals |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-58e4c0efd07a4bb79f494539371cd22d2025-08-20T02:05:20ZengWileyNeural Plasticity2090-59041687-54432020-01-01202010.1155/2020/94364069436406A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early ADHao Liu0Haimeng Hu1Huiying Wang2Jiahui Han3Yunfei Li4Huihui Qi5Meimei Wang6Sisi Zhang7Huijin He8Xiaohu Zhao9Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Imaging, Huashan Hospital, Fudan University, Shanghai, ChinaOphthalmology Department, Huashan Hospital, Fudan University, Shanghai, ChinaCollege of Optical and Electronic Technology, China Jiliang University, Hangzhou, ChinaDepartment of Imaging, The Fifth People’s Hospital of Shanghai, Fudan University, Shanghai, ChinaDepartment of Imaging, Shanghai Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, ChinaDepartment of Imaging, Shanghai Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, ChinaDepartment of Imaging, Shanghai Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, ChinaDepartment of Imaging, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Imaging, The Fifth People’s Hospital of Shanghai, Fudan University, Shanghai, ChinaMost previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.http://dx.doi.org/10.1155/2020/9436406 |
| spellingShingle | Hao Liu Haimeng Hu Huiying Wang Jiahui Han Yunfei Li Huihui Qi Meimei Wang Sisi Zhang Huijin He Xiaohu Zhao A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD Neural Plasticity |
| title | A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD |
| title_full | A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD |
| title_fullStr | A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD |
| title_full_unstemmed | A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD |
| title_short | A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD |
| title_sort | brain network constructed on an l1 norm regression model is more sensitive in detecting small world network changes in early ad |
| url | http://dx.doi.org/10.1155/2020/9436406 |
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