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Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits
Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572942/ https://www.ncbi.nlm.nih.gov/pubmed/28878666 http://dx.doi.org/10.3389/fnagi.2017.00279 |
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author | Mai, Naikeng Zhong, Xiaomei Chen, Ben Peng, Qi Wu, Zhangying Zhang, Weiru Ouyang, Cong Ning, Yuping |
author_facet | Mai, Naikeng Zhong, Xiaomei Chen, Ben Peng, Qi Wu, Zhangying Zhang, Weiru Ouyang, Cong Ning, Yuping |
author_sort | Mai, Naikeng |
collection | PubMed |
description | Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients. |
format | Online Article Text |
id | pubmed-5572942 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55729422017-09-06 Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits Mai, Naikeng Zhong, Xiaomei Chen, Ben Peng, Qi Wu, Zhangying Zhang, Weiru Ouyang, Cong Ning, Yuping Front Aging Neurosci Neuroscience Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients. Frontiers Media S.A. 2017-08-23 /pmc/articles/PMC5572942/ /pubmed/28878666 http://dx.doi.org/10.3389/fnagi.2017.00279 Text en Copyright © 2017 Mai, Zhong, Chen, Peng, Wu, Zhang, Ouyang and Ning. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Mai, Naikeng Zhong, Xiaomei Chen, Ben Peng, Qi Wu, Zhangying Zhang, Weiru Ouyang, Cong Ning, Yuping Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits |
title | Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits |
title_full | Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits |
title_fullStr | Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits |
title_full_unstemmed | Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits |
title_short | Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits |
title_sort | weight rich-club analysis in the white matter network of late-life depression with memory deficits |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572942/ https://www.ncbi.nlm.nih.gov/pubmed/28878666 http://dx.doi.org/10.3389/fnagi.2017.00279 |
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