Major Depressive Disorder, Other Mood Disorders, and Suicide

2017 ◽  
pp. 120-164
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mood Disorders ◽  
Major Depressive

scholarly journals Cortical Thickness in Individuals at High Familial Risk of Mood Disorders as They Develop Major Depressive Disorder

2015 ◽  
Vol 78 (1) ◽  
pp. 58-66 ◽  
Author(s):  
Martina Papmeyer ◽  
Stephen Giles ◽  
Jessica E. Sussmann ◽  
Shauna Kielty ◽  
Tiffany Stewart ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mood Disorders ◽  
Cortical Thickness ◽  
Familial Risk ◽  
Major Depressive

Family study of the aggregation of eating disorders and mood disorders

2003 ◽  
Vol 33 (7) ◽  
pp. 1319-1323 ◽  
Author(s):  
B. MANGWETH ◽  
J. I. HUDSON ◽  
H. G. POPE ◽  
A. HAUSMANN ◽  
C. De COL ◽  
...  
Keyword(s):  
Eating Disorders ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mood Disorders ◽  
Family Study ◽  
Familial Aggregation ◽  
Interview Study ◽  
Major Depressive ◽  
Causal Factors ◽  
Family Interview

Background. Family studies have suggested that eating disorders and mood disorders may coaggregate in families. To study further this question, data from a family interview study of probands with and without major depressive disorder was examined.Method. A bivariate proband predictive logistic regression model was applied to data from a family interview study, conducted in Innsbruck, Austria, of probands with (N=64) and without (N=58) major depressive disorder, together with 330 of their first-degree relatives.Results. The estimated odds ratio (OR) for the familial aggregation of eating disorders (anorexia nervosa, bulimia nervosa and binge-eating disorder) was 7·0 (95% CI 1·4, 28; P=0·006); the OR for the familial aggregation of mood disorders (major depression and bipolar disorder) was 2·2 (0·92, 5·4; P=0·076); and for the familial coaggregation of eating disorders with mood disorders the OR was 2·2 (1·1, 4·6; P=0·035).Conclusions. The familial coaggregation of eating disorders with mood disorders was significant and of the same magnitude as the aggregation of mood disorders alone – suggesting that eating disorders and mood disorders have common familial causal factors.

scholarly journals Second messenger/signal transduction pathways in major mood disorders: moving from membrane to mechanism of action, part I: major depressive disorder

CNS Spectrums ◽  
2013 ◽  
Vol 18 (5) ◽  
pp. 231-241 ◽  
Author(s):  
Mark J. Niciu ◽  
Dawn F. Ionescu ◽  
Daniel C. Mathews ◽  
Erica M. Richards ◽  
Carlos A. Zarate
Keyword(s):  
Signal Transduction ◽  
Major Depressive Disorder ◽  
Amino Acid ◽  
Depressive Disorder ◽  
Mood Disorders ◽  
Mechanism Of Action ◽  
Time Lag ◽  
Second Messenger ◽  
Mood Stabilizers ◽  
Major Depressive

The etiopathogenesis and treatment of major mood disorders have historically focused on modulation of monoaminergic (serotonin, norepinephrine, dopamine) and amino acid [γ-aminobutyric acid (GABA), glutamate] receptors at the plasma membrane. Although the activation and inhibition of these receptors acutely alter local neurotransmitter levels, their neuropsychiatric effects are not immediately observed. This time lag implicates intracellular neuroplasticity as primary in the mechanism of action of antidepressants and mood stabilizers. The modulation of intracellular second messenger/signal transduction cascades affects neurotrophic pathways that are both necessary and sufficient for monoaminergic and amino acid–based treatments. In this review, we will discuss the evidence in support of intracellular mediators in the pathophysiology and treatment of preclinical models of despair and major depressive disorder (MDD). More specifically, we will focus on the following pathways: cAMP/PKA/CREB, neurotrophin-mediated (MAPK and others), p11, Wnt/Fz/Dvl/GSK3β, and NFκB/ΔFosB. We will also discuss recent discoveries with rapidly acting antidepressants, which activate the mammalian target of rapamycin (mTOR) and release of inhibition on local translation via elongation factor stimulation. Throughout this discourse, we will highlight potential intracellular targets for therapeutic intervention. Finally, future clinical implications are discussed.

scholarly journals EEG Frontal Asymmetry in Dysthymia, Major Depressive Disorder and Euthymic Bipolar Disorder

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2414
Author(s):  
Chiara Spironelli ◽  
Francesca Fusina ◽  
Marco Bortolomasi ◽  
Alessandro Angrilli
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mood Disorders ◽  
Quantitative Eeg ◽  
Beta Activity ◽  
Major Depressive ◽  
Eeg Asymmetry ◽  
Frontal Asymmetry ◽  
Frontal Eeg Asymmetry

In the last few decades, the incidence of mood disorders skyrocketed worldwide and has brought an increasing human and economic burden. Depending on the main symptoms and their evolution across time, they can be classified in several clinical subgroups. A few psychobiological indices have been extensively investigated as promising markers of mood disorders. Among these, frontal asymmetry measured at rest with quantitative EEG has represented the main available marker in recent years. Only a few studies so far attempted to distinguish the features and differences among diagnostic types of mood disorders by using this index. The present study measured frontal EEG asymmetry during a 5-min resting state in three samples of patients with bipolar disorder in a Euthymic phase (EBD, n = 17), major depressive disorder (MDD, n = 25) and persistent depressive disorder (PDD, n = 21), once termed dysthymia. We aimed to test the hypothesis that MDD and PDD lack the typical leftward asymmetry exhibited by normal as well as EBD patients, and that PDD shows greater clinical and neurophysiological impairments than MDD. Clinical scales revealed no symptoms in EBD, and significant larger anxiety and depression scores in PDD than in MDD patients. Relative beta (i.e., beta/alpha ratio) EEG asymmetry was measured from lateral frontal sites and results revealed the typical greater left than right frontal beta activity in EBD, as well as a lack of asymmetry in both MDD and PDD. The last two groups also had lower bilateral frontal beta activity in comparison with the EBD group. Results concerning group differences were interpreted by taking into account both the clinical and the neurophysiological domains.

Important Issues in Understanding Comorbidity Between Generalized Anxiety Disorder and Major Depressive Disorder

2014 ◽  
Author(s):  
Susan Mineka ◽  
Deepika Anand ◽  
Jennifer A. Sumner
Keyword(s):  
Major Depressive Disorder ◽  
Anxiety Disorder ◽  
Depressive Disorder ◽  
Mood Disorder ◽  
Mood Disorders ◽  
Generalized Anxiety Disorder ◽  
Generalized Anxiety ◽  
Major Depressive ◽  
Cross Sectional ◽  
The Past

The comorbidity of anxiety and mood disorders has been of great interest to psychopathology researchers for the past 25 years. One topic––the comorbidity of generalized anxiety disorder (GAD) and major depressive disorder (MDD)––has received considerable attention, in part because it has raised fundamental nosological issues regarding whether GAD should continue to be categorized as an anxiety disorder or whether it should be recategorized as a mood disorder. We review the logic for reclassifying GAD with the mood disorders as well as what we believe to be even more compelling reasons for why it should be retained as an anxiety disorder. In doing so, we review three different kinds of comorbidity—cross-sectional, cumulative (lifetime), and sequential. We also discuss overlaps and distinctions in what is known about the etiology of GAD and MDD and how their somewhat different cognitive and affective profiles bear on these issues of classification. Finally, we briefly discuss what some of the treatment implications may be for individuals with comorbid GAD and MDD.

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

2020 ◽  
Vol 52 (1) ◽  
pp. 38-51
Author(s):  
Caglar Uyulan ◽  
Türker Tekin Ergüzel ◽  
Huseyin Unubol ◽  
Merve Cebi ◽  
Gokben Hizli Sayar ◽  
...  
Keyword(s):  
Neural Network ◽  
Major Depressive Disorder ◽  
Deep Learning ◽  
Depressive Disorder ◽  
Convolutional Neural Network ◽  
Mood Disorders ◽  
Major Depressive ◽  
Frequency Bands ◽  
Functional Connections ◽  
Translational Biomarkers

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

scholarly journals Age-Related Increase in the Number of Oligodendrocytes Is Dysregulated in Schizophrenia and Mood Disorders

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Victor Vostrikov ◽  
Natalya Uranova
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Prefrontal Cortex ◽  
White Matter ◽  
Depressive Disorder ◽  
Mood Disorders ◽  
Numerical Density ◽  
Major Depressive ◽  
Age Related ◽  
Related Increase

The postnatal maturation of the human prefrontal cortex is associated with substantial increase of number of oligodendrocytes. Previously, we reported decreased numerical density of oligodendrocytes in the prefrontal cortex in schizophrenia and mood disorders. To gain further understanding of the role oligodendrocytes in pathogenesis of schizophrenia and mood disorders, we examined the effect of the age on the number of oligodendrocytes in the prefrontal cortex in schizophrenia, bipolar disorder, and major depressive disorder. We revealed the age-related increase in numerical density of oligodendrocytes in layer VI and adjacent white matter of BA10 and BA 9 in normal controls but not in schizophrenia, bipolar disorder, and major depressive disorder. The absence of normal increase in the number of oligodendrocytes in gray and white matter with age in schizophrenia and mood disorders suggests that age-related process of oligodendrocyte increase is dysregulated in schizophrenia and mood disorders.

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