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Antidepressant Effects of Electroconvulsive Therapy Unrelated to the Brain's Functional Network Connectivity alterations at an Individual Level.
Chen, Guang-Dong; Ji, Feng; Li, Gong-Ying; Lyu, Bo-Xuan; Hu, Wei; Zhuo, Chuan-Jun.
Affiliation
  • Chen GD; Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang 325000, China.
  • Ji F; Department of Mental Health, Jining Medical University, Jining, Shandong 272076, China.
  • Li GY; Department of Mental Health, Jining Medical University, Jining, Shandong 272076, China.
  • Lyu BX; Department of Genetic Laboratory, Beijing Jiashibosi Technology Co., Ltd., Beijing 100000, China.
  • Hu W; Department of Information, China Potevio Information Industry Company Limited, Beijing 100080, China.
  • Zhuo CJ; Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
Chin Med J (Engl) ; 130(4): 414-419, 2017 02 20.
Article in En | MEDLINE | ID: mdl-28218214
ABSTRACT

BACKGROUND:

Electroconvulsive therapy (ECT) can alleviate the symptoms of treatment-resistant depression (TRD). Functional network connectivity (FNC) is a newly developed method to investigate the brain's functional connectivity patterns. The first aim of this study was to investigate FNC alterations between TRD patients and healthy controls. The second aim was to explore the relationship between the ECT treatment response and pre-ECT treatment FNC alterations in individual TRD patients.

METHODS:

This study included 82 TRD patients and 41 controls. Patients were screened at baseline and after 2 weeks of treatment with a combination of ECT and antidepressants. Group information guided-independent component analysis (GIG-ICA) was used to compute subject-specific functional networks (FNs). Grassmann manifold and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks' connectivity patterns (FCP). Pearson's correlation analysis was used to calculate the correlations between the FCP and ECT response.

RESULTS:

A total of 82 TRD patients in the ECT group were successfully treated. On an average, 8.50 ± 2.00 ECT sessions were conducted. After ECT treatment, only 42 TRD patients had an improved response to ECT (the Hamilton scores reduction rate was more than 50%), response rate 51%. 8 FNs (anterior and posterior default mode network, bilateral frontoparietal network, audio network, visual network, dorsal attention network, and sensorimotor network) were obtained using GIG-ICA. We did not found that FCPs were significantly different between TRD patients and healthy controls. Moreover, the baseline FCP was unrelated to the ECT treatment response.

CONCLUSIONS:

The FNC was not significantly different between the TRD patients and healthy controls, and the baseline FCP was unrelated to the ECT treatment response. These findings will necessitate that we modify the experimental scheme to explore the mechanisms underlying ECT's effects on depression and explore the specific predictors of the effects of ECT based on the pre-ECT treatment magnetic resonance imaging.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Depression / Electroconvulsive Therapy Type of study: Prognostic_studies Limits: Adult / Female / Humans / Male / Middle aged Language: En Journal: Chin Med J (Engl) Year: 2017 Document type: Article Affiliation country: China Publication country: CHINA / CN / REPUBLIC OF CHINA

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Depression / Electroconvulsive Therapy Type of study: Prognostic_studies Limits: Adult / Female / Humans / Male / Middle aged Language: En Journal: Chin Med J (Engl) Year: 2017 Document type: Article Affiliation country: China Publication country: CHINA / CN / REPUBLIC OF CHINA