Cognitive styles in individuals with bipolar disorders

2003 ◽  
Vol 33 (6) ◽  
pp. 1081-1088 ◽  
Author(s):  
JAN SCOTT ◽  
MARIE POPE
Keyword(s):  
Bipolar Disorder ◽  
Cognitive Style ◽  
Emotional Processing ◽  
Network Models ◽  
Bipolar Disorders ◽  
Self Esteem ◽  
Small Sample ◽  
Dysfunctional Attitudes ◽  
Clinical Samples ◽  
Neural Network Models

Background. Published studies of emotional processing and cognitive style in bipolar disorders tend to have small sample sizes or use non-clinical samples. Larger clinically representative studies are needed.Method. Self-esteem, dysfunctional attitudes and personality style were compared in unipolar (N=16) and bipolar disorder (N=77); and then investigated in the different phases of bipolar disorder (remitted=26; depressed=38; hypomanic=13). One-year outcome was assessed in 36 bipolar subjects.Results. Unipolar subjects and bipolar subjects differed significantly in their mean levels of negative self-esteem (unipolar=15·5; bipolar=12·7; P<0·05). Bipolar subjects with hypomania reported mean levels of dysfunctional beliefs that were higher than individuals in remission but lower than depressed subjects (remitted=136·7; depressed=153·8; hypomanic=144·8; P<0·05). Hypomanic subjects recorded the highest levels of negative as well as positive self-esteem. In the exploratory analysis of outcome, negative self-esteem (Exp [B] 1·91; 95% CI 1·11 to 3·32; P<0·05) was the most robust predictor of relapse.Conclusions. There are similarities in the cognitive style of individuals with unipolar as compared to bipolar disorders. Cognitive style in hypomania represents a phase between remission and depression rather than the polar opposite of depression. The implications of these findings are considered for psychological and neural network models.

At-risk-states for bipolar disorder

2011 ◽  
Vol 26 (S2) ◽  
pp. 2099-2099
Author(s):  
P. Conus
Keyword(s):  
At Risk ◽  
Bipolar Disorder ◽  
Psychotic Disorders ◽  
Bipolar Disorders ◽  
Small Sample ◽  
First Episode ◽  
Prodromal Phase ◽  
Ultra High Risk ◽  
Risk Patients ◽  
New Research

Early intervention strategies have been developed over the past 20 years for psychotic disorders and recent studies have proven their efficacy. However, most of the attention has been drawn to schizophrenia, and affective psychoses have been neglected. In the recent past, new research has identified a similar need for earlier intervention in bipolar disorders, and prodrome to bipolar disorder has emerged as a key domain to investigate. Despite the complexity of this issue due to the lack of a clear consensus regarding definitions of the various stages of the disorder, some progress has been made in this domain.Two recent retrospective studies have identified a prodromal phase to first episode mania lasting between 6 and 18 months, and have identified a range of symptoms that occur during this period. It is however likely that on the basis of symptomatic profile, identification of at risk patients would be difficult, considering their low specificity. Two complementary directions have been recently proposed in order to refine such an approach. The first strategy, based on at-risk profiles inspired by the Ultra High Risk concept developed for psychosis, has yielded some promising results on a small sample of patients. The second is based on the combination of identified risk symptoms with both risk factors and markers of vulnerability into a First Episode Mania Prodrome Inventory which is currently under validation.

scholarly journals Epidemiological and clinical aspects will guide the neuroimaging research in bipolar disorder

2015 ◽  
Vol 24 (2) ◽  
pp. 117-120 ◽  
Author(s):  
J. Houenou ◽  
C. Perlini ◽  
P. Brambilla
Keyword(s):  
Bipolar Disorder ◽  
Emotional Processing ◽  
Small Sample Size ◽  
Epidemiological Studies ◽  
Small Sample ◽  
Clinical Aspects ◽  
Diagnostic Categories ◽  
Parahippocampal Cortex ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Although neurobiological mechanisms of bipolar disorder (BD) are still unclear, neural models of the disease have recently been conceptualised thanks to neuroimaging. Indeed, magnetic resonance imaging (MRI) studies investigating structural and functional connectivity between different areas of the brain suggest an altered prefrontal–limbic coupling leading to disrupted emotional processing in BD, including uncinate fasciculus, amygdala, parahippocampal cortex, cingulate cortex as well corpus callosum. Specifically, these models assume an altered prefrontal control over a hyperactivity of the subcortical limbic structures implicated in automatic emotional processing. This impaired mechanism may finally trigger emotional hyper-reactivity and mood episodes. In this review, we first summarised some key neuroimaging studies on BD. In the second part of the work, we focused on the heterogeneity of the available studies. This variability is partly due to methodological factors (i.e., small sample size) and differences among studies (i.e., MRI acquisition and post-processing analyses) and partly to the clinical heterogeneity of BD. We finally outlined how epidemiological studies should indicate which risk factors and clinical dimensions of BD are relevant to be studied with neuroimaging in order to reduce heterogeneity and go beyond diagnostic categories.

Symptom-specific self-referential cognitive processes in bipolar disorder: a longitudinal analysis

2012 ◽  
Vol 43 (9) ◽  
pp. 1895-1907 ◽  
Author(s):  
H. Pavlickova ◽  
F. Varese ◽  
O. Turnbull ◽  
J. Scott ◽  
R. Morriss ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Multilevel Models ◽  
Rating Scale ◽  
Self Esteem ◽  
Dysfunctional Attitudes ◽  
Psychological Processes ◽  
Co Morbidity ◽  
Clinical Measures ◽  
Specific Symptoms ◽  
Over Time

BackgroundAlthough depression and mania are often assumed to be polar opposites, studies have shown that, in patients with bipolar disorder, they are weakly positively correlated and vary somewhat independently over time. Thus, when investigating relationships between specific psychological processes and specific symptoms (mania and depression), co-morbidity between the symptoms and changes over time must be taken into account.MethodA total of 253 bipolar disorder patients were assessed every 24 weeks for 18 months using the Hamilton Rating Scale for Depression (HAMD), the Bech–Rafaelsen Mania Assessment Scale (MAS), the Rosenberg Self-Esteem Questionnaire (RSEQ), the Dysfunctional Attitudes Scale (DAS), the Internal, Personal and Situational Attributions Questionnaire (IPSAQ) and the Personal Qualities Questionnaire (PQQ). We calculated multilevel models using the xtreg module of Stata 9.1, with psychological and clinical measures nested within each participant.ResultsMania and depression were weakly, yet significantly, associated; each was related to distinct psychological processes. Cross-sectionally, self-esteem showed the most robust associations with depression and mania: depression was associated with low positive and high negative self-esteem, and mania with high positive self-esteem. Depression was significantly associated with most of the other self-referential measures, whereas mania was weakly associated only with the externalizing bias of the IPSAQ and the achievement scale of the DAS. Prospectively, low self-esteem predicted future depression.ConclusionsThe associations between different self-referential thinking processes and different phases of bipolar disorder, and the presence of the negative self-concept in both depression and mania, have implications for therapeutic management, and also for future directions of research.

scholarly journals Postpartum psychosis in bipolar disorder: no evidence of association with personality traits, cognitive style or affective temperaments

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
A. Perry ◽  
K. Gordon-Smith ◽  
I. Webb ◽  
E. Fone ◽  
A. Di Florio ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Personality Traits ◽  
Cognitive Style ◽  
Cognitive Styles ◽  
Dysfunctional Attitudes ◽  
Postpartum Psychosis ◽  
Affective Temperaments ◽  
Increased Risk ◽  
Affective Temperament ◽  
History Of

Abstract Background Bipolar disorder has been associated with several personality traits, cognitive styles and affective temperaments. Women who have bipolar disorder are at increased risk of experiencing postpartum psychosis, however little research has investigated these traits and temperaments in relation to postpartum psychosis. The aim of this study is to establish whether aspects of personality, cognitive style and affective temperament that have been associated with bipolar disorder also confer vulnerability to postpartum psychosis over and above their known association with bipolar disorder. Methods Personality traits (neuroticism, extraversion, schizotypy and impulsivity), cognitive styles (low self-esteem and dysfunctional attitudes) and affective temperaments (including cyclothymic and depressive temperaments) were compared between two groups of parous women with DSM-IV bipolar I disorder: i) 284 with a lifetime history of postpartum psychosis within 6 weeks of delivery (PP group), ii) 268 without any history of mood episodes with onset during pregnancy or within 6 months of delivery (no perinatal mood episode, No PME group). Results After controlling for current mood state, and key demographic, clinical and pregnancy-related variables, there were no statistically significant differences between the PP and No PME groups on any of the personality, cognitive style or affective temperament measures. Conclusions Personality traits, cognitive styles and affective temperaments previously shown to be associated with bipolar disorder in general were not specifically associated with the occurrence of postpartum psychosis. These factors may not be relevant for predicting risk of postpartum psychosis in women with bipolar disorder.

scholarly journals Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

2021 ◽  
Vol 13 (22) ◽  
pp. 4694
Author(s):  
Guangzhi Rong ◽  
Kaiwei Li ◽  
Yulin Su ◽  
Zhijun Tong ◽  
Xingpeng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Landslide Susceptibility ◽  
Small Sample Size ◽  
Network Models ◽  
Small Sample ◽  
Landslide Risk ◽  
Susceptibility Assessment ◽  
Neural Network Models ◽  
Landslide Susceptibility Assessment ◽  
Geological Conditions

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.

scholarly journals Cognitive style in bipolar disorder

2005 ◽  
Vol 187 (5) ◽  
pp. 431-437 ◽  
Author(s):  
Lisa Jones ◽  
Jan Scott ◽  
Sayeed Haque ◽  
Katherine Gordon-Smith ◽  
Jessica Heron ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Cognitive Style ◽  
Affective Disorder ◽  
Mental State ◽  
Affective Disorders ◽  
Depressive Symptomatology ◽  
Bipolar Disorders ◽  
Unipolar Depression ◽  
Healthy Controls ◽  
Recurrent Depression

BackgroundAbnormalities of cognitive style in bipolar disorder are of both clinical and theoretical importance.AimsTo compare cognitive style in people with affective disorders and in healthy controls.MethodSelf-rated questionnaires were administered to 118 individuals with bipolar I disorder, 265 with unipolar major recurrent depression and 268 healthy controls. Those with affective disorder were also interviewed using the Schedules for Clinical Assessment in Neuropsychiatry and case notes were reviewed.ResultsThose with bipolar disorder and those with unipolar depression demonstrated different patterns of cognitive style from controls; negative self-esteem best discriminated between those with affective disorders and controls; measures of cognitive style were substantially affected by current levels of depressive symptomatology; patterns of cognitive style were similar in bipolar and unipolar disorder when current mental state was taken into account.ConclusionsThose with affective disorder significantly differed from controls on measures of cognitive style but there were no differences between unipolar and bipolar disorders when current mental state was taken into account.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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