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Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning
Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with n...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829502/ https://www.ncbi.nlm.nih.gov/pubmed/33505243 http://dx.doi.org/10.3389/fnins.2020.627062 |
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author | Zhang, Gaoyan Li, Yuexuan Zhang, Xiaodong Huang, Lixiang Cheng, Yue Shen, Wen |
author_facet | Zhang, Gaoyan Li, Yuexuan Zhang, Xiaodong Huang, Lixiang Cheng, Yue Shen, Wen |
author_sort | Zhang, Gaoyan |
collection | PubMed |
description | Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers. In this study, we aimed to use multi-layer modular algorithm to study abnormality in dynamic graph properties of brain network in MHE patients and construct a machine learning model to identify individual MHE from noHE. Here, a time length of 500-second resting-state functional MRI data were collected from 41 healthy subjects, 32 noHE patients and 30 MHE patients. Multi-layer modular algorithm was performed on dynamic brain functional connectivity graph. The connection-stability score was used to characterize the loyalty in each brain network module. Nodal flexibility, cohesion and disjointness were calculated to describe how the node changes the network affiliation across time. Results show that significant differences between MHE and noHE were found merely in nodal disjointness in higher cognitive network modules (ventral attention, fronto-parietal, default mode networks) and these abnormalities were associated with the decline in patients’ attention and visual memory function evaluated by Digit Symbol Test. Finally, feature extraction from node disjointness with the support vector machine classifier showed an accuracy of 88.71% in discrimination of MHE from noHE, which was verified by different window sizes, modular partition parameters and machine learning parameters. All these results show that abnormal nodal disjointness in higher cognitive networks during brain network evolution can be seemed as a biomarker for identification of MHE, which help us understand the disease mechanism of MHE at a fine scale. |
format | Online Article Text |
id | pubmed-7829502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78295022021-01-26 Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning Zhang, Gaoyan Li, Yuexuan Zhang, Xiaodong Huang, Lixiang Cheng, Yue Shen, Wen Front Neurosci Neuroscience Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers. In this study, we aimed to use multi-layer modular algorithm to study abnormality in dynamic graph properties of brain network in MHE patients and construct a machine learning model to identify individual MHE from noHE. Here, a time length of 500-second resting-state functional MRI data were collected from 41 healthy subjects, 32 noHE patients and 30 MHE patients. Multi-layer modular algorithm was performed on dynamic brain functional connectivity graph. The connection-stability score was used to characterize the loyalty in each brain network module. Nodal flexibility, cohesion and disjointness were calculated to describe how the node changes the network affiliation across time. Results show that significant differences between MHE and noHE were found merely in nodal disjointness in higher cognitive network modules (ventral attention, fronto-parietal, default mode networks) and these abnormalities were associated with the decline in patients’ attention and visual memory function evaluated by Digit Symbol Test. Finally, feature extraction from node disjointness with the support vector machine classifier showed an accuracy of 88.71% in discrimination of MHE from noHE, which was verified by different window sizes, modular partition parameters and machine learning parameters. All these results show that abnormal nodal disjointness in higher cognitive networks during brain network evolution can be seemed as a biomarker for identification of MHE, which help us understand the disease mechanism of MHE at a fine scale. Frontiers Media S.A. 2021-01-11 /pmc/articles/PMC7829502/ /pubmed/33505243 http://dx.doi.org/10.3389/fnins.2020.627062 Text en Copyright © 2021 Zhang, Li, Zhang, Huang, Cheng and Shen. 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) and the copyright owner(s) 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 Zhang, Gaoyan Li, Yuexuan Zhang, Xiaodong Huang, Lixiang Cheng, Yue Shen, Wen Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning |
title | Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning |
title_full | Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning |
title_fullStr | Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning |
title_full_unstemmed | Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning |
title_short | Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning |
title_sort | identifying mild hepatic encephalopathy based on multi-layer modular algorithm and machine learning |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829502/ https://www.ncbi.nlm.nih.gov/pubmed/33505243 http://dx.doi.org/10.3389/fnins.2020.627062 |
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