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Diagnosing schizophrenia with network analysis and a machine learning method
OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research. METHODS: We investigated 48 schi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7051840/ https://www.ncbi.nlm.nih.gov/pubmed/32022360 http://dx.doi.org/10.1002/mpr.1818 |
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author | Jo, Young Tak Joo, Sung Woo Shon, Seung‐Hyun Kim, Harin Kim, Yangsik Lee, Jungsun |
author_facet | Jo, Young Tak Joo, Sung Woo Shon, Seung‐Hyun Kim, Harin Kim, Yangsik Lee, Jungsun |
author_sort | Jo, Young Tak |
collection | PubMed |
description | OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research. METHODS: We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls. RESULTS: In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns. CONCLUSION: In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia. |
format | Online Article Text |
id | pubmed-7051840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70518402020-03-05 Diagnosing schizophrenia with network analysis and a machine learning method Jo, Young Tak Joo, Sung Woo Shon, Seung‐Hyun Kim, Harin Kim, Yangsik Lee, Jungsun Int J Methods Psychiatr Res Original Articles OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research. METHODS: We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls. RESULTS: In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns. CONCLUSION: In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia. John Wiley and Sons Inc. 2020-02-05 /pmc/articles/PMC7051840/ /pubmed/32022360 http://dx.doi.org/10.1002/mpr.1818 Text en © 2020 The Authors. International Journal of Methods in Psychiatric Research Published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Jo, Young Tak Joo, Sung Woo Shon, Seung‐Hyun Kim, Harin Kim, Yangsik Lee, Jungsun Diagnosing schizophrenia with network analysis and a machine learning method |
title | Diagnosing schizophrenia with network analysis and a machine learning method |
title_full | Diagnosing schizophrenia with network analysis and a machine learning method |
title_fullStr | Diagnosing schizophrenia with network analysis and a machine learning method |
title_full_unstemmed | Diagnosing schizophrenia with network analysis and a machine learning method |
title_short | Diagnosing schizophrenia with network analysis and a machine learning method |
title_sort | diagnosing schizophrenia with network analysis and a machine learning method |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7051840/ https://www.ncbi.nlm.nih.gov/pubmed/32022360 http://dx.doi.org/10.1002/mpr.1818 |
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