Classification Of Mood Disorders Research Articles

Projection of Bipolarity to Mitochondria: GABA Shunt is related with Self pathology and Medical Comorbidity in Bipolar Disorder

The nature of the relationship with the self-object is the source of mental energy. At this point, the self is a spatiotemporal formation. The self has been found to be associated with the GAD enzyme activity responsible for GABA production at the molecular level. GABA is a metabolite of Krebs. Bipolar disorder is a biphasic state of energy dysregulation as a circadian rithms. Phasic nature of mitochondria to produce ATP may be crucial to the switching of affective states in bipolar disorder. The GABA shunt is an alternative energy production pathway that is activated in response to stress. The GABA shunt plays an important role in preventing the accumulation of reactive oxygen intermediates and cell death. On the other hand, GABA shunt impairs phosphorylation processes. Mitochondrial dysfunction is reflected in glucose, lipid and protein metabolism. A bipolar spectrum is possible at this point, including physical illnesses. A classification of mood disorders based on entropy levels is framed by medical comorbidity.

Feature distribution-based Medical Data Augmentation: enhancing Mood Disorder Classification

Feature distribution-based Medical Data Augmentation: enhancing Mood Disorder Classification

Existential depression: A meaningful diagnostic entity?

To examine the construct of existential depression and whether it represents a distinct diagnostic entity. Descriptive psychopathology and phenomenology are used to define the characteristics of existential depression and for comparison with other presentations of low mood. Existential depression can be differentiated from other forms of depression by careful appraisal of symptomatology. Drawing attention to this, and likewise other distinguishable yet under-recognised forms of depression, may help stimulate interest in further research on the classification of mood disorders with the prospect of greater diagnostic specificity and more precise treatment matching. Existential depression is a clinically discernible diagnostic entity.

Non-Invasive Bio-Signal Data Classification Of Psychiatric Mood Disorders Using Modified CNN and VGG16

In this study, the aim is to develop an ensemble machine learning (ML) based deep learning (DL) model classifiers to detect and compare one type of major psychiatric disorders of mood disorders (Depressive and Bipolar disorders) using Electroencephalography (EEG). The diverse and multiple non-invasive biosignals were collected retrospectively according to the granted ethical permission. The experimental part is consisted from three main parts. First part is the data collection&development, the second part is data transformation and augmentation via Spectrogram image conversion process and online Keras data augmentation part, respectively. The third and final part is to fed these image dataset into modified Convolutional Neural Network (CNN) and VGG16 models for training and testing parts to detect, compare and discriminate mood disorders types in detail with a specific healthy group. As the performance evaluation background of the mood disorder classification models, confusion matrices and receiver operating characteristics (ROC) curves were used and finally, the accuracy achieved by CNN model was 88% and VGG16 model was %90, which is an improvement of 10% compared to the previous studies in literature. Therefore, our system can help clinicians and researchers to manage, diagnose and prognosis of the mental health of people.

Latent subtypes of manic and/or irritable episode symptoms in two population-based cohorts.

Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart. To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability. We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores. Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including 'minimally affected', 'inactive restless', active restless', 'focused creative' and 'extensively affected' individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment. Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to a) construct a model which ensembles risk/resilience (R/R), adverse outcome pathways (AOPs), staging, and the phenome of mood disorders, and b) disclose new classes based on these feature sets. This study was conducted using data of 67 healthy controls and 105 mood disordered patients. The R/R ratio, assessed as a combination of the paraoxonase 1 (PON1) gene, PON1 enzymatic activity, and early life time trauma (ELT), predicted the high-density lipoprotein cholesterol - paraoxonase 1 complex (HDL-PON1), reactive oxygen and nitrogen species (RONS), nitro-oxidative stress toxicity (NOSTOX), staging (number of depression and hypomanic episodes and suicidal attempts), and phenome (the Hamilton Depression and Anxiety scores and the Clinical Global Impression; current suicidal ideation; quality of life and disability measurements) scores. Partial Least Squares pathway analysis showed that 44.2% of the variance in the phenome was explained by ELT, RONS/NOSTOX, and staging scores. Cluster analysis conducted on all those feature sets discovered two distinct patient clusters, namely 69.5% of the patients were allocated to a class with high R/R, RONS/NOSTOX, staging, and phenome scores, and 30.5% to a class with increased staging and phenome scores. This classification cut across the bipolar (BP1/BP2) and major depression disorder classification and was more distinctive than the latter classifications. We constructed a nomothetic network model which reunited all features of mood disorders into a mechanistically transdiagnostic model.

Towards understanding the impacts of anxiety on attention and visual working memory

Anxiety can refer to a dispositional trait characteristic, a transient emotional state, or a classification of mood disorders that can be broadly defined by a range of key aspects. Individuals with more anxious dispositions will more frequently experience apprehension and worry, leading to more frequent experiences of anxious states, particularly under stressful conditions (e.g. Eysenck et al., 2007). Anxious states can be characterized by these apprehensions, as well as the experience of somatic hyperarousal and tension (e.g. Moran, 2016; Ree et al., 2008). The estimated 12-month prevalence of anxiety disorders in the UK was 18.1% of the population in 2010 (Fineberg et al., 2013). Though, in Europe and North America, estimates of those who experience chronic anxiety below the threshold for a clinical diagnosis has been suggested to be twice that of diagnosed cases (Haller et al., 2014). Even within nonclinical groups, anxiety is associated with reduced personal, social and professional functioning (Haller et al., 2014). Additionally, there is a longstanding body of evidence suggesting that anxiety is related to impaired attentional control (e.g. Eysenck et al., 2007, Berggren & Derakshan, 2013), which may limit working memory capacity (Moran, 2016). Despite this, studies of the impact of anxiety on working memory, particularly in the visual domain, remain relatively few in number. The purpose of this review is to highlight our developing understanding of the impacts of anxiety on cognition, and the future directions that research in this area may usefully take.

Conceptual issues in mood disorder: An update.

The concept of mood disorders dates back to eternity. This long history of the emotions and their disorders has a fascinating journey. As with any history concerned with disease concepts, the conceptual history of mood disorders must be scrutinized through close attention to nosological texts in tracing how categories were created how they evolved over the time. This article covers the evolution of the concept of mood disorders and its journey from the Hippocratic era to the Kraepelin's dichotomization between dementia praecox (schizophrenia) and manic-depressive insanity (bipolar and unipolar disorders) and its culmination into the contemporary classifications of mood disorders. It also addresses the problem of boundaries between the different mood disorders in terms of their conceptualization and classification. The shortcomings of the classificatory systems and the current progress in the nosology have also been discussed.

Relationships between recurrence and polarity in major depressive disorders: Pooled analysis of the BRIDGE and BRIDGE-II-MIX cohorts

Backgroundcurrent classifications of mood disorders focus on polarity rather than recurrence, separating bipolar disorder from major depressive disorder (MDD). The aim of the present study is to explore the possible relationships between number and frequency of depressive episodes and clinical variables associated to bipolarity, in a large sample of MDD patients. Methodsthe clinical characteristics of 7055 patients with MDD were analyzed and compared according to the number and frequency of depressive episodes. Two stepwise backward logistic regression model were used to identify the predictive value of clinical features based on the presence of high number (≥3 episodes) and high frequency (≥3 episodes/year) of depressive episodes. Resultshigh-recurrence and high-frequency MDD patients showed greater family history for bipolar disorder, higher prevalence of psychotic features, more suicide attempts, higher rates of treatment resistance and mood switches with antidepressants (ADs) and higher rates of bipolarity diagnosis according to Angst criteria, compared to low-recurrence and low-frequency patients. Logistic regressions showed that a brief current depressive episode, a previous history of treatment resistance and AD-induced mood switches, a diagnosis of bipolarity and comorbid borderline personality disorder were the variables associated with both high-recurrence and high-frequency depression. Limitationsthe study participating centers were not randomly selected and several variables were retrospectively assessed. Conclusionseven in the absence of hypomanic/manic episodes, high-recurrence and high-frequency MDD seem to be in continuity with the bipolar spectrum disorders in terms of clinical features and, perhaps, treatment response.

Exploring Macroscopic and Microscopic Fluctuations of Elicited Facial Expressions for Mood Disorder Classification

In the clinical diagnosis of mood disorder, a large proportion of patients with bipolar disorder (BD) are misdiagnosed as having unipolar depression (UD). Generally, long-term tracking is required for patients with BD to conduct an appropriate diagnosis by using traditional diagnosis tools. A one-time diagnosis system for facilitating diagnosis procedures is thus highly desirable. Accordingly, in this study, the facial expressions of patients with BD, patients with UD, and healthy controls elicited by emotional video clips were used for conducting mood disorder classification; the classification was performed by exploring the temporal fluctuation characteristics among the three groups. First, macroscopic facial expressions characterized by action units (AUs) were applied for describing the temporal transformation of muscles. Modulation spectrum analysis was applied to extract short-term intensity variations in the AUs. An interval-based multilayer perceptron (MLP) neural network was then used to classify mood disorder on the basis of the detected AU intensities. Moreover, motion vectors (MVs) were employed to describe subtle changes in facial expressions in the microscopic view. Eight basic orientations of MV change were considered for representing microfluctuation. Wavelet decomposition was then applied to extract entropy and energy features in different frequency bands. A long short-term memory model was finally used to model long-term variations for conducting mood disorder classification. A decision-level fusion approach was conducted on the combined results of macroscopic and microscopic facial expressions. For evaluating the described methods, the facial expressions elicited from the 36 subjects (12 from each of the BD, UD, and control groups) were used in 12-fold cross-validation experiments. Approaches for macroscopic and microscopic expressions achieved classification accuracies of 63.9 and 66.7 percent, respectively, and the accuracy of the fusion approach reached 72.2 percent. The results indicate that macroscopic and microscopic view descriptors are complementary to each other and helpful for conducting mood disorder classification.

Cognitive symptoms in depression and clinical outcomes in a 6-month non-interventional, prospective study of antidepressant treatment in China

current classifications of mood disorders focus on polarity rather than recurrence, separating bipolar disorder from major depressive disorder (MDD). The aim of the present study is to explore the possible relationships between number and frequency of depressive episodes and clinical variables associated to bipolarity, in a large sample of MDD patients.the clinical characteristics of 7055 patients with MDD were analyzed and compared according to the number and frequency of depressive episodes. Two stepwise backward logistic regression model were used to identify the predictive value of clinical features based on the presence of high number (≥3 episodes) and high frequency (≥3 episodes/year) of depressive episodes.high-recurrence and high-frequency MDD patients showed greater family history for bipolar disorder, higher prevalence of psychotic features, more suicide attempts, higher rates of treatment resistance and mood switches with antidepressants (ADs) and higher rates of bipolarity diagnosis according to Angst criteria, compared to low-recurrence and low-frequency patients. Logistic regressions showed that a brief current depressive episode, a previous history of treatment resistance and AD-induced mood switches, a diagnosis of bipolarity and comorbid borderline personality disorder were the variables associated with both high-recurrence and high-frequency depression.the study participating centers were not randomly selected and several variables were retrospectively assessed.even in the absence of hypomanic/manic episodes, high-recurrence and high-frequency MDD seem to be in continuity with the bipolar spectrum disorders in terms of clinical features and, perhaps, treatment response.

USING EXPERIMENTAL METHODS TO UNDERSTAND THE CONSEQUENCES OF INDIVIDUALIZED GENETIC EXPLANATIONS FOR MENTAL DISORDERS

current classifications of mood disorders focus on polarity rather than recurrence, separating bipolar disorder from major depressive disorder (MDD). The aim of the present study is to explore the possible relationships between number and frequency of depressive episodes and clinical variables associated to bipolarity, in a large sample of MDD patients.the clinical characteristics of 7055 patients with MDD were analyzed and compared according to the number and frequency of depressive episodes. Two stepwise backward logistic regression model were used to identify the predictive value of clinical features based on the presence of high number (≥3 episodes) and high frequency (≥3 episodes/year) of depressive episodes.high-recurrence and high-frequency MDD patients showed greater family history for bipolar disorder, higher prevalence of psychotic features, more suicide attempts, higher rates of treatment resistance and mood switches with antidepressants (ADs) and higher rates of bipolarity diagnosis according to Angst criteria, compared to low-recurrence and low-frequency patients. Logistic regressions showed that a brief current depressive episode, a previous history of treatment resistance and AD-induced mood switches, a diagnosis of bipolarity and comorbid borderline personality disorder were the variables associated with both high-recurrence and high-frequency depression.the study participating centers were not randomly selected and several variables were retrospectively assessed.even in the absence of hypomanic/manic episodes, high-recurrence and high-frequency MDD seem to be in continuity with the bipolar spectrum disorders in terms of clinical features and, perhaps, treatment response.

Cognitive function and lifetime features of depression and bipolar disorder in a large population sample: Cross-sectional study of 143,828 UK Biobank participants

BackgroundThis study investigated differences in cognitive performance between middle-aged adults with and without a lifetime history of mood disorder features, adjusting for a range of potential confounders. MethodsCross-sectional analysis of baseline data from the UK Biobank cohort. Adults aged 40–69 (n=143,828) were assessed using measures of reasoning, reaction time and memory. Self-reported data on lifetime features of major depression and bipolar disorder were used to construct groups for comparison against controls. Regression models examined the association between mood disorder classification and cognitive performance, adjusting for sociodemographic, lifestyle and clinical confounders. ResultsInverse associations between lifetime history of bipolar or severe recurrent depression features and cognitive performance were attenuated or reversed after adjusting for confounders, including psychotropic medication use and current depressive symptoms. Participants with a lifetime history of single episode or moderate recurrent depression features outperformed controls to a small (but statistically significant) degree, independent of adjustment for confounders. There was a significant interaction between use of psychotropic medication and lifetime mood disorder features, with reduced cognitive performance observed in participants taking psychotropic medication. ConclusionsIn this general population sample of adults in middle age, lifetime features of recurrent depression or bipolar disorder were only associated with cognitive impairment within unadjusted analyses. These findings underscore the importance of adjusting for potential confounders when investigating mood disorder-related cognitive function.

Unipolar mania: a necessary diagnostic concept.

In the classification of mood disorders, major depressive disorder is separate from bipolar disorders whereas mania is not. Studies on pure mania are therefore rare. Our paper reviews the evidence for distinguishing pure mania (M) and mania with mild depression (Md) from bipolar disorder. Two large epidemiological studies found a prevalence of 1.7-1.8% of M/Md in adolescents and adults. Several clinical follow-up studies demonstrated good stability of the diagnosis after a previous history of three manic episodes. Compared to bipolar disorder, manic disorder is characterised by a weaker family history for depression, an earlier onset, fewer recurrences and better remission, and is less comorbid with anxiety disorders. In addition, mania is strongly associated with a hyperthymic temperament, manifests more psychotic symptoms and is more often treated with antipsychotics. Twin and family studies find mania to be more heritable than depression and show no significant transmission from depression to mania or from mania to depression. Cardiovascular mortality is elevated among patients with mood disorders generally and is highest among those with mania. In non-Western countries, mania and the manic episodes in bipolar disorder are reported to occur more frequently than in Western countries.

Classification of mood disorders.

This paper looks at some recent developments in the official diagnostic definitions (DSM-5) and in the research domain. The spectrum concept of mood disorders consists of the components of depression and mania, alone or in combination, on a continuum. Its international operational classification changes regularly, being based on symptoms, their duration and consequences. Causation is as yet unknown. DSM-5 excludes unipolar mania and mania with mild depression as separate diagnoses (they come under bipolar I and bipolar II disorders) and introduces a new hierarchy of manic symptoms, placing energy/activity above mood (elated, irritable). This is shown to be problematic on the basis of recent data. The validity of the duration criteria for mania (1 week), hypomania (4 days) and depression (2 weeks) is also seriously questioned. Shorter episodes are clinically very relevant. The definition of mania/hypomania is a persistent problem, contributing to frequent un- derdiagnosis of bipolar disorder in depressed patients. Other contributory factors include that patients often do not feel ill or seek treatment for the consequences of their high mood, and that hypomania can be hidden by substance use disorders (SUD). Hidden hypomanic syndromes are important because associated with treatment resistance, high comorbidity with anxiety/panic and SUD, psychotic and cognitive symptoms, dementia and higher mortality. Anxiety, too, is doubtless a mood disorder but there is still no concept which integrates anxiety with bipolar disorder and depression.

Initial interpretation and evaluation of a profile-based classification system for the anxiety and mood disorders: Incremental validity compared to DSM-IV categories.

Limitations in anxiety and mood disorder diagnostic reliability and validity due to the categorical approach to classification used by the Diagnostic and Statistical Manual of Mental Disorders (DSM) have been long recognized. Although these limitations have led researchers to forward alternative classification schemes, few have been empirically evaluated. In a sample of 1,218 outpatients with anxiety and mood disorders, the present study examined the validity of Brown and Barlow's (2009) proposal to classify the anxiety and mood disorders using an integrated dimensional-categorical approach based on transdiagnostic emotional disorder vulnerabilities and phenotypes. Latent class analyses of 7 transdiagnostic dimensional indicators suggested that a 6-class (i.e., profile) solution provided the best model fit and was the most conceptually interpretable. Interpretation of the classes was further supported when compared with DSM diagnoses (i.e., within-class prevalence of diagnoses, using diagnoses to predict class membership). In addition, hierarchical multiple regression models were used to demonstrate the incremental validity of the profiles; class probabilities consistently accounted for unique variance in anxiety and mood disorder outcomes above and beyond DSM diagnoses. These results provide support for the potential development and utility of a hybrid dimensional-categorical profile approach to anxiety and mood disorder classification. In particular, the availability of dimensional indicators and corresponding profiles may serve as a useful complement to DSM diagnoses for both researchers and clinicians.

The DSM‐5 classification of mood disorders: some fallacies and fault lines

The <scp>DSM</scp>‐5 classification of mood disorders: some fallacies and fault lines

Following the development of ICD-11 through World Psychiatry (and other sources).

Following the development of ICD-11 through World Psychiatry (and other sources).

Human and Animal Brain Phospholipids Fatty Acids, Evolution and Mood Disorders

The purpose of this paper is to connect the experimental evidence concerning brain phospholipids fatty acids composition by comparing the first warm-blooded animal in the phylogeny (birds) with the human brain at various ages of life (from the fetal period until the eightieth year of age). The particularity of our investigation is an almost unique opportunity for groped a hypothesis about the evolutionary aspects of the behavior of brain and consciousness, as represented in the human and animal world, as a result of the evidence that led to the diagnostic classification of mood disorders in humans, in their similarity with some animal species. A logical sequence of considerations about the mood disorder diagnosis, due to unequivocal evidence by the use of mathematical tools that cannot be manipulated, it leads to results that most probably indicate and suggest the existence of a common brain “biochemical house“, in man and animal. This “common house” will become more and more complex, during evolution, from animal to man, respecting the concept of the molecular equilibrium and allowing to each living being the adaptation to their needs and their roles. Small deviations from the biochemical equilibrium of brain fatty acids can manifest pathological behavioral responses, much amplified. Everything seems to be witnessed by the strong classificatory correspondence of the platelets fatty acids which correspond to psycho pathologies, especially for the Linoleic acid and alpha Linolenic acid, in particular the Linoleic Acid, which, to varying percentages, it may correspond to psychopathological phenomena.

Bipolar disorders in DSM-5: strengths, problems and perspectives.

The diagnostic classification of mood disorders by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) had two major shortcomings: an underdiagnosis of bipolar disorders and a large proportion of treated patients had to be allocated to the vague NOS groups ‘not otherwise specified’. Several new subthreshold groups of depression, bipolar disorders and mixed states are now operationally defined in DSM-5. In addition, hypomanic and manic episodes occurring during antidepressant treatments are, under certain conditions, accepted as criteria for bipolar disorders. The diagnosis of bipolarity now requires, as entry criterion A, not only the presence of elated or irritable mood but also the association of these symptoms with increased energy/activity. This restriction will unfortunately change the diagnoses of some patients from DSM-IV bipolar I and II disorders to subdiagnostic bipolar syndromes. Nonetheless, overall, DSM-5 is a step in the right direction, specifying more subdiagnostic categories with an improved dimensional approach to severity. DSM-5 may also have an impact on patient selection for placebo-controlled drug trials with antidepressants.