scholarly journals The classification of depression: are we still confused?

2008 ◽  
Vol 192 (2) ◽  
pp. 83-85 ◽  
Author(s):  
James Cole ◽  
Peter McGuffin ◽  
Anne E. Farmer

SummaryRecent developments in the classification of major depressive disorder are reviewed in light of the predictions made by Kendell in the 1970s. Particularly, the institution of operational diagnoses along with the contentious issues of subdividing major depressive disorder and its characterisation on a dimensional as opposed to a categorical scale.

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.


2017 ◽  
Author(s):  
Dajiang Zhu ◽  
Qingyang Li ◽  
Brandalyn C. Riedel ◽  
Neda Jahanshad ◽  
Derrek P. Hibar ◽  
...  

2013 ◽  
Vol 19 (5) ◽  
pp. 370-377
Author(s):  
Eugene K. O. Wong ◽  
Rajeev Krishnadas ◽  
Jonathan Cavanagh

SummaryThere has been a surge in interest in the boundaries of psychiatry, both as a specialty in its own right and as a branch of medicine separate from neurology. Our article expands on this, giving examples of how recent developments in neuroimmunology can be beneficial for psychiatry, using multiple sclerosis (MS) as an example. We also provide a summary of literature on novel research in the treatment of depression using anti-inflammatory agents. Finally, we suggest approaches to the identification and management of major depressive disorder in patients with MS or other physical illnesses, and how this translates to general psychiatric practice.


Author(s):  
Galina Surova ◽  
Christine Ulke ◽  
Frank Martin Schmidt ◽  
Tilman Hensch ◽  
Christian Sander ◽  
...  

AbstractFatigue is considered a key symptom of major depressive disorder (MDD), yet the term lacks specificity. It can denote a state of increased sleepiness and lack of drive (i.e., downregulated arousal) as well as a state of high inner tension and inhibition of drive with long sleep onset latencies (i.e., upregulated arousal), the latter typically found in depression. It has been proposed to differentiate fatigue along the dimension of brain arousal. We investigated whether such stratification within a group of MDD patients would reveal a subgroup with distinct clinical features. Using an automatic classification of EEG vigilance stages, an arousal stability score was calculated for 15-min resting EEGs of 102 MDD patients with fatigue. 23.5% of the patients showed signs of hypoarousal with EEG patterns indicating drowsiness or sleep; this hypoaroused subgroup was compared with remaining patients (non-hypoaroused subgroup) concerning self-rated measures of depressive symptoms, sleepiness, and sleep. The hypoaroused subgroup scored higher on the Beck Depression Inventory items “loss of energy” (Z = − 2.13, p = 0.033; ɳ2 = 0.044, 90% CI 0.003–0.128) and “concentration difficulty” (Z = − 2.40, p = 0.017; ɳ2 = 0.056, 90% CI 0.009–0.139), and reported higher trait and state sleepiness (p < 0.05) as compared to the non-hypoaroused group. The non-hypoaroused subgroup, in contrast, reported more frequently the presence of suicidal ideation (Chi2 = 3.81, p = 0.051; ɳ2 = 0.037, 90% CI 0.0008–0.126). In this study, we found some evidence that stratifying fatigued MDD patients by arousal may lead to subgroups that are pathophysiologically and clinically more homogeneous. Brain arousal may be a worth while target in clinical research for better understanding the mechanisms underlying suicidal tendencies and to improve treatment response.


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