scholarly journals Shared facial emotion processing functional network findings in medication-naïve major depressive disorder and healthy individuals: detection by sICA

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Jian Li ◽  
E. Kale Edmiston ◽  
Yanqing Tang ◽  
Guoguang Fan ◽  
Ke Xu ◽  
...  
2015 ◽  
Vol 23 (3) ◽  
pp. 304-313 ◽  
Author(s):  
Emily M. Briceño ◽  
Lisa J. Rapport ◽  
Michelle T. Kassel ◽  
Linas A. Bieliauskas ◽  
Jon-Kar Zubieta ◽  
...  

2020 ◽  
Author(s):  
Hye In Woo ◽  
Jisook Park ◽  
Shinn-Won Lim ◽  
Doh Kwan Kim ◽  
Soo-Youn Lee

Abstract Background: Major depressive disorder (MDD), common mental disorder, lacks objective diagnostic and prognosis biomarkers. The objective of this study was to perform proteomic analysis to identify proteins with changed expression levels after antidepressant treatment and investigate differences in protein expression between MDD patients and healthy individuals.Methods: A total of 111 proteins obtained from literature review were subjected to multiple reaction monitoring (MRM)-based protein quantitation. Finally, seven proteins were quantified for plasma specimens of 10 healthy controls and 78 MDD patients (those at baseline and at 6 weeks after antidepressant treatment of either selective serotonin reuptake inhibitors (SSRIs) or mirtazapine). Results: Among 78 MDD patients, 35 patients were treated with SSRIs and 43 patients were treated with mirtazapine. Nineteen (54.3%) and 16 (37.2%) patients responded to SSRIs and mirtazapine, respectively. Comparing MDD patients with healthy individuals, alteration of transthyretin was observed in MDD (p = 0.026). There was no significant difference in protein levels related to SSRIs treatment. Plasma thyroxine-binding globulin (TBG) was different between before and after mirtazapine treatment only in responders (p = 0.007).Conclusions: In proteomic analysis of plasma specimens from MDD patients, transthyretin and TBG levels were altered in MDD and changed after antidepressant treatment.


Author(s):  
Masakazu Higuchi ◽  
Shinichi Tokuno ◽  
Mitsuteru Nakamura ◽  
Shuji Shinohara ◽  
Shunji Mitsuyoshi ◽  
...  

Objective: In this study, we propose a voice index to identify healthy individuals, patients with bipolar disorder, and patients with major depressive disorder using polytomous logistic regression analysis.Methods: Voice features were extracted from voices of healthy individuals and patients with mental disease. Polytomous logistic regression analysis was performed for some voice features.Results: With the prediction model obtained using the analysis, we identified subject groups and were able to classify subjects into three groups with 90.79% accuracy.Conclusion: These results show that the proposed index may be used as a new evaluation index to identify depression.


2012 ◽  
Vol 26 (11) ◽  
pp. 1424-1433 ◽  
Author(s):  
Gabriela Rosenblau ◽  
Philipp Sterzer ◽  
Meline Stoy ◽  
Soyoung Park ◽  
Eva Friedel ◽  
...  

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