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Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder
BACKGROUND: Major depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis. OBJECTIVE: This study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine lea...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644563/ https://www.ncbi.nlm.nih.gov/pubmed/37957613 http://dx.doi.org/10.1186/s12888-023-05349-9 |
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author | Huang, Yuanyuan Yi, Yun Chen, Qiang Li, Hehua Feng, Shixuan Zhou, Sumiao Zhang, Ziyun Liu, Chenyu Li, Junhao Lu, Qiuling Zhang, Lida Han, Wei Wu, Fengchun Ning, Yuping |
author_facet | Huang, Yuanyuan Yi, Yun Chen, Qiang Li, Hehua Feng, Shixuan Zhou, Sumiao Zhang, Ziyun Liu, Chenyu Li, Junhao Lu, Qiuling Zhang, Lida Han, Wei Wu, Fengchun Ning, Yuping |
author_sort | Huang, Yuanyuan |
collection | PubMed |
description | BACKGROUND: Major depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis. OBJECTIVE: This study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine learning. METHODS: Forty first-episode and drug-naïve patients with MDD and forty healthy controls (HCs) were recruited. EEG data were collected from all subjects in the resting state with eyes closed for 10 min. The severity of MDD was assessed by the Hamilton Depression Rating Scale (HAMD-17). Machine learning analysis was used to identify the patients with MDD. RESULTS: Compared to the HC group, the relative power of the low delta and theta bands was significantly higher in the right occipital region, and the relative power of the alpha band in the entire posterior occipital region was significantly lower in the MDD group. In the MDD group, the alpha band scalp functional connectivity was overall lower, while the scalp functional connectivity in the gamma band was significantly higher than that in the HC group. In the feature set of the relative power of the ROI in each band, the highest accuracy of 88.2% was achieved using the KNN classifier while using PCA feature selection. In the explanatory model using SHAP values, the top-ranking influence feature is the relative power of the alpha band in the left parietal region. CONCLUSIONS: Our findings reveal that the abnormal EEG neural oscillations may reflect an imbalance of excitation, inhibition and hyperactivity in the cerebral cortex in first-episode and drug-naïve patients with MDD. The relative power of the alpha band in the left parietal region is expected to be an objective electrophysiological indicator of MDD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-023-05349-9. |
format | Online Article Text |
id | pubmed-10644563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106445632023-11-13 Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder Huang, Yuanyuan Yi, Yun Chen, Qiang Li, Hehua Feng, Shixuan Zhou, Sumiao Zhang, Ziyun Liu, Chenyu Li, Junhao Lu, Qiuling Zhang, Lida Han, Wei Wu, Fengchun Ning, Yuping BMC Psychiatry Research BACKGROUND: Major depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis. OBJECTIVE: This study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine learning. METHODS: Forty first-episode and drug-naïve patients with MDD and forty healthy controls (HCs) were recruited. EEG data were collected from all subjects in the resting state with eyes closed for 10 min. The severity of MDD was assessed by the Hamilton Depression Rating Scale (HAMD-17). Machine learning analysis was used to identify the patients with MDD. RESULTS: Compared to the HC group, the relative power of the low delta and theta bands was significantly higher in the right occipital region, and the relative power of the alpha band in the entire posterior occipital region was significantly lower in the MDD group. In the MDD group, the alpha band scalp functional connectivity was overall lower, while the scalp functional connectivity in the gamma band was significantly higher than that in the HC group. In the feature set of the relative power of the ROI in each band, the highest accuracy of 88.2% was achieved using the KNN classifier while using PCA feature selection. In the explanatory model using SHAP values, the top-ranking influence feature is the relative power of the alpha band in the left parietal region. CONCLUSIONS: Our findings reveal that the abnormal EEG neural oscillations may reflect an imbalance of excitation, inhibition and hyperactivity in the cerebral cortex in first-episode and drug-naïve patients with MDD. The relative power of the alpha band in the left parietal region is expected to be an objective electrophysiological indicator of MDD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-023-05349-9. BioMed Central 2023-11-13 /pmc/articles/PMC10644563/ /pubmed/37957613 http://dx.doi.org/10.1186/s12888-023-05349-9 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Huang, Yuanyuan Yi, Yun Chen, Qiang Li, Hehua Feng, Shixuan Zhou, Sumiao Zhang, Ziyun Liu, Chenyu Li, Junhao Lu, Qiuling Zhang, Lida Han, Wei Wu, Fengchun Ning, Yuping Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder |
title | Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder |
title_full | Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder |
title_fullStr | Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder |
title_full_unstemmed | Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder |
title_short | Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder |
title_sort | analysis of eeg features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644563/ https://www.ncbi.nlm.nih.gov/pubmed/37957613 http://dx.doi.org/10.1186/s12888-023-05349-9 |
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