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Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naïve major depressive disorder

Overview of attention for article published in Brain Imaging and Behavior, February 2018
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Title
Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naïve major depressive disorder
Published in
Brain Imaging and Behavior, February 2018
DOI 10.1007/s11682-018-9845-9
Pubmed ID
Authors

Jiajia Zhu, Huanhuan Cai, Yonggui Yuan, Yingying Yue, Deguo Jiang, Ce Chen, Wei Zhang, Chuanjun Zhuo, Yongqiang Yu

Abstract

Several behavioral and neuroimaging markers could be used to predict eventual antidepressant medication (ADM) outcomes in patients with major depressive disorder (MDD). However, these predictors are either subjective or complex, which has limited their clinical use. Thus, we aimed to identify an objective and easy-to-get marker to predict early therapeutic efficacy. Forty-seven drug-naïve patients with MDD and 47 age-, gender- and education-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scans. We calculated the variable coefficient (VC) of the global signal for each subject. Baseline Hamilton Rating Scale for Depression (HRSD) score and that after 2 weeks of ADM were assessed for patients. Although there was no difference in VC between patients with MDD and healthy controls, we found a significant positive correlation between the VC and the decline rate of HRSD scores in the patients. Compared with the non-responding depression (NRD) group, the treatment-responsive depression (TRD) group had a higher VC. Receiver operator characteristic curve analysis revealed that the VC exhibited a good ability to differentiate TRD from NRD. In addition, the linear and logistic regression analyses showed that the VC was a significant predictor of the decline rate of HRSD scores and the antidepressant treatment response. These findings suggest that variance of the global signal may serve as a useful marker to help clinicians find an appropriate drug for individuals with MDD at the earliest opportunity and then further to facilitate personalized therapy.

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The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 21%
Student > Bachelor 6 13%
Researcher 5 10%
Other 3 6%
Professor 2 4%
Other 4 8%
Unknown 18 38%
Readers by discipline Count As %
Psychology 12 25%
Medicine and Dentistry 6 13%
Neuroscience 5 10%
Nursing and Health Professions 2 4%
Computer Science 1 2%
Other 2 4%
Unknown 20 42%