Cargando…
Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification
Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM net...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147725/ https://www.ncbi.nlm.nih.gov/pubmed/37117256 http://dx.doi.org/10.1038/s41598-023-33199-8 |
_version_ | 1785034851965992960 |
---|---|
author | Cheng, Ping Li, Yadi Wang, Gaoyan Dong, Haibo Liu, Huifen Shen, Wenwen Zhou, Wenhua |
author_facet | Cheng, Ping Li, Yadi Wang, Gaoyan Dong, Haibo Liu, Huifen Shen, Wenwen Zhou, Wenhua |
author_sort | Cheng, Ping |
collection | PubMed |
description | Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models. |
format | Online Article Text |
id | pubmed-10147725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101477252023-04-30 Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification Cheng, Ping Li, Yadi Wang, Gaoyan Dong, Haibo Liu, Huifen Shen, Wenwen Zhou, Wenhua Sci Rep Article Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models. Nature Publishing Group UK 2023-04-28 /pmc/articles/PMC10147725/ /pubmed/37117256 http://dx.doi.org/10.1038/s41598-023-33199-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cheng, Ping Li, Yadi Wang, Gaoyan Dong, Haibo Liu, Huifen Shen, Wenwen Zhou, Wenhua Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification |
title | Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification |
title_full | Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification |
title_fullStr | Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification |
title_full_unstemmed | Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification |
title_short | Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification |
title_sort | aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147725/ https://www.ncbi.nlm.nih.gov/pubmed/37117256 http://dx.doi.org/10.1038/s41598-023-33199-8 |
work_keys_str_mv | AT chengping aberranttopologyofwhitematternetworksinpatientswithmethamphetaminedependenceanditsapplicationinsupportvectormachinebasedclassification AT liyadi aberranttopologyofwhitematternetworksinpatientswithmethamphetaminedependenceanditsapplicationinsupportvectormachinebasedclassification AT wanggaoyan aberranttopologyofwhitematternetworksinpatientswithmethamphetaminedependenceanditsapplicationinsupportvectormachinebasedclassification AT donghaibo aberranttopologyofwhitematternetworksinpatientswithmethamphetaminedependenceanditsapplicationinsupportvectormachinebasedclassification AT liuhuifen aberranttopologyofwhitematternetworksinpatientswithmethamphetaminedependenceanditsapplicationinsupportvectormachinebasedclassification AT shenwenwen aberranttopologyofwhitematternetworksinpatientswithmethamphetaminedependenceanditsapplicationinsupportvectormachinebasedclassification AT zhouwenhua aberranttopologyofwhitematternetworksinpatientswithmethamphetaminedependenceanditsapplicationinsupportvectormachinebasedclassification |