Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study
Background: Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a usefu...
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| Format: | Article |
| Language: | English |
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Elsevier
2018-01-01
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| Series: | NeuroImage: Clinical |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158218301955 |
| _version_ | 1811328303849537536 |
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| author | Miseon Shim Chang-Hwan Im Yong-Wook Kim Seung-Hwan Lee |
| author_facet | Miseon Shim Chang-Hwan Im Yong-Wook Kim Seung-Hwan Lee |
| author_sort | Miseon Shim |
| collection | DOAJ |
| description | Background: Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD). Method: Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales. Results: At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales. Conclusion: Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD. Keywords: Major depressive disorder, Electroencephalogram, Brain electrical activity mapping, Source-level brain network |
| first_indexed | 2024-04-13T15:23:56Z |
| format | Article |
| id | doaj.art-fbc5c02d4d2e4595b3cf6cc8e4477fa0 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-04-13T15:23:56Z |
| publishDate | 2018-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-fbc5c02d4d2e4595b3cf6cc8e4477fa02022-12-22T02:41:35ZengElsevierNeuroImage: Clinical2213-15822018-01-011910001007Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram studyMiseon Shim0Chang-Hwan Im1Yong-Wook Kim2Seung-Hwan Lee3Psychiatry Department, University of Missouri, Kansas City, USA; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of KoreaDepartment of Biomedical Engineering, Hanyang University, Seoul, Republic of KoreaDepartment of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of KoreaClinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, Republic of Korea; Corresponding author at: 170, Juhwa-ro, Ilsanseo-gu, Goyang-si, Gyeonggi-do 10380, Republic of Korea.Background: Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD). Method: Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales. Results: At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales. Conclusion: Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD. Keywords: Major depressive disorder, Electroencephalogram, Brain electrical activity mapping, Source-level brain networkhttp://www.sciencedirect.com/science/article/pii/S2213158218301955 |
| spellingShingle | Miseon Shim Chang-Hwan Im Yong-Wook Kim Seung-Hwan Lee Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study NeuroImage: Clinical |
| title | Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study |
| title_full | Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study |
| title_fullStr | Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study |
| title_full_unstemmed | Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study |
| title_short | Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study |
| title_sort | altered cortical functional network in major depressive disorder a resting state electroencephalogram study |
| url | http://www.sciencedirect.com/science/article/pii/S2213158218301955 |
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