Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder

作     者：Ju, Yumeng Wang, Mi Liu, Jin Liu, Bangshan Yan, Danfeng Lu, Xiaowen Sun, Jinrong Dong, Qiangli Zhang, Liang Guo, Hua Zhao, Futao Liao, Mei Zhang, Li Zhang, Yan Li, Lingjiang 

作者机构：Cent South Univ Dept Psychiat Changsha 410011 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Natl Clin Res Ctr Mental Disorders Changsha 410011 Hunan Peoples R China Cent South Univ China Natl Technol Inst Mental Disorders Hunan Technol Inst Psychiat Hunan Key Lab Psychiat & Mental HlthMental Hlth I Changsha 410011 Hunan Peoples R China Zhumadian Psychiat Hosp Zhumadian 463000 Henan Peoples R China 

出 版 物：《PSYCHOLOGICAL MEDICINE》 (心理医学)

年 卷 期：2023年第53卷第13期

页      面：5963-5975页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 1002[医学-临床医学] 10[医学] 

基　　金：We would like to thank all the patients and healthy volunteers who participated in our study 

主　　题：Antidepressant machine learning major depressive disorder (MDD) recurrence resting-state functional connectivity treatment non-response 

摘      要：BackgroundTreatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in ***-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in *** measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year *** findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.