Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder

Author:

Peng Yueheng1ORCID,Huang Yang2ORCID,Chen Baodan1,He Mengling1,Jiang Lin1ORCID,Li Yuqin1,Huang Xunan1,Pei Changfu1,Zhang Shu1,Li Cunbo1ORCID,Zhang Xiabing1ORCID,Zhang Tao1ORCID,Zheng Yutong1,Yao Dezhong1ORCID,Li Fali1ORCID,Xu Peng1ORCID

Affiliation:

1. MOE Key Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China

2. Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville, Knoxville, TN, USA

Funder

National Natural Science Foundation of China

Project of Science and Technology Department of Sichuan Province

Science and Technology Development Fund, Macau, SAR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

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2. Evaluating the depression state during perinatal period by non-invasive scalp EEG;Cerebral Cortex;2024-01-31

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4. Graph model of phase lag index for connectivity analysis in EEG of emotions;2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS);2023-06

5. AI-assisted prediction of differential response to antidepressant classes using electronic health records;npj Digital Medicine;2023-04-26

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