Cargando…
Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity
At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully captu...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209471/ https://www.ncbi.nlm.nih.gov/pubmed/34149345 http://dx.doi.org/10.3389/fnins.2021.651439 |
_version_ | 1783709135843360768 |
---|---|
author | Zhao, Zongya Li, Jun Niu, Yanxiang Wang, Chang Zhao, Junqiang Yuan, Qingli Ren, Qiongqiong Xu, Yongtao Yu, Yi |
author_facet | Zhao, Zongya Li, Jun Niu, Yanxiang Wang, Chang Zhao, Junqiang Yuan, Qingli Ren, Qiongqiong Xu, Yongtao Yu, Yi |
author_sort | Zhao, Zongya |
collection | PubMed |
description | At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ. |
format | Online Article Text |
id | pubmed-8209471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82094712021-06-18 Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity Zhao, Zongya Li, Jun Niu, Yanxiang Wang, Chang Zhao, Junqiang Yuan, Qingli Ren, Qiongqiong Xu, Yongtao Yu, Yi Front Neurosci Neuroscience At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ. Frontiers Media S.A. 2021-06-03 /pmc/articles/PMC8209471/ /pubmed/34149345 http://dx.doi.org/10.3389/fnins.2021.651439 Text en Copyright © 2021 Zhao, Li, Niu, Wang, Zhao, Yuan, Ren, Xu and Yu. https://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 Zhao, Zongya Li, Jun Niu, Yanxiang Wang, Chang Zhao, Junqiang Yuan, Qingli Ren, Qiongqiong Xu, Yongtao Yu, Yi Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity |
title | Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity |
title_full | Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity |
title_fullStr | Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity |
title_full_unstemmed | Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity |
title_short | Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity |
title_sort | classification of schizophrenia by combination of brain effective and functional connectivity |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209471/ https://www.ncbi.nlm.nih.gov/pubmed/34149345 http://dx.doi.org/10.3389/fnins.2021.651439 |
work_keys_str_mv | AT zhaozongya classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT lijun classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT niuyanxiang classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT wangchang classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT zhaojunqiang classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT yuanqingli classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT renqiongqiong classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT xuyongtao classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity AT yuyi classificationofschizophreniabycombinationofbraineffectiveandfunctionalconnectivity |