Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder
Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therape...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9872032/ |
| _version_ | 1797805105159864320 |
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| author | Yueheng Peng Yang Huang Baodan Chen Mengling He Lin Jiang Yuqin Li Xunan Huang Changfu Pei Shu Zhang Cunbo Li Xiabing Zhang Tao Zhang Yutong Zheng Dezhong Yao Fali Li Peng Xu |
| author_facet | Yueheng Peng Yang Huang Baodan Chen Mengling He Lin Jiang Yuqin Li Xunan Huang Changfu Pei Shu Zhang Cunbo Li Xiabing Zhang Tao Zhang Yutong Zheng Dezhong Yao Fali Li Peng Xu |
| author_sort | Yueheng Peng |
| collection | DOAJ |
| description | Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD. |
| first_indexed | 2024-03-13T05:47:08Z |
| format | Article |
| id | doaj.art-a7f7deacab8543ef848ba9f942406cad |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:47:08Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-a7f7deacab8543ef848ba9f942406cad2023-06-13T20:08:42ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302577258810.1109/TNSRE.2022.32030739872032Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive DisorderYueheng Peng0https://orcid.org/0000-0001-5389-8609Yang Huang1https://orcid.org/0000-0001-9336-5479Baodan Chen2Mengling He3Lin Jiang4https://orcid.org/0000-0002-7892-118XYuqin Li5Xunan Huang6Changfu Pei7Shu Zhang8Cunbo Li9https://orcid.org/0000-0002-1954-6113Xiabing Zhang10https://orcid.org/0000-0002-4275-3663Tao Zhang11https://orcid.org/0000-0002-2891-4213Yutong Zheng12Dezhong Yao13https://orcid.org/0000-0002-8042-879XFali Li14https://orcid.org/0000-0002-2450-4591Peng Xu15https://orcid.org/0000-0002-7932-0386MOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Electrical Engineering and Computer Science, University of Tennessee Knoxville, Knoxville, TN, USAMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, ChinaMedication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.https://ieeexplore.ieee.org/document/9872032/Major depressive disorderresting-state EEGclinical therapyprediction |
| spellingShingle | Yueheng Peng Yang Huang Baodan Chen Mengling He Lin Jiang Yuqin Li Xunan Huang Changfu Pei Shu Zhang Cunbo Li Xiabing Zhang Tao Zhang Yutong Zheng Dezhong Yao Fali Li Peng Xu Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder IEEE Transactions on Neural Systems and Rehabilitation Engineering Major depressive disorder resting-state EEG clinical therapy prediction |
| title | Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder |
| title_full | Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder |
| title_fullStr | Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder |
| title_full_unstemmed | Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder |
| title_short | Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder |
| title_sort | electroencephalographic network topologies predict antidepressant responses in patients with major depressive disorder |
| topic | Major depressive disorder resting-state EEG clinical therapy prediction |
| url | https://ieeexplore.ieee.org/document/9872032/ |
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