Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression
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ABSTRACT:
SUBMITTER: Tan G
PROVIDER: S-EPMC10808829 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:The recently discovered default mode network (DMN) is a group of areas in the human brain characterized, collectively, by functions of a self-referential nature. In normal individuals, activity in the DMN is reduced during nonself-referential goal-directed tasks, in keeping with the folk-psychological notion of losing one's self in one's work. Imaging and anatomical studies in major depression have found alterations in both the structure and function in some regions that belong to the DMN, thus, suggesting a basis for the disordered self-referential thought of depression. Here, we sought to examine DMN functionality as a network in patients with major depression, asking whether the ability to regulate its activity and, hence, its role in self-referential processing, was impaired. To do so, we asked patients and controls to examine negative pictures passively and also to reappraise them actively. In widely distributed elements of the DMN [ventromedial prefrontal cortex prefrontal cortex (BA 10), anterior cingulate (BA 24/32), lateral parietal cortex (BA 39), and lateral temporal cortex (BA 21)], depressed, but not control subjects, exhibited a failure to reduce activity while both looking at negative pictures and reappraising them. Furthermore, looking at negative pictures elicited a significantly greater increase in activity in other DMN regions (amygdala, parahippocampus, and hippocampus) in depressed than in control subjects. These data suggest depression is characterized by both stimulus-induced heightened activity and a failure to normally down-regulate activity broadly within the DMN. These findings provide a brain network framework within which to consider the pathophysiology of depression.
| S-EPMC2631078 | biostudies-literature
Project description:IntroductionPersonality-based profiling helps elucidate associations between psychopathology symptoms and address shortcomings of current nosologies. The objective of this study was to bracket the assumption of a priori diagnostic class borders and apply the profiling approach to a transdiagnostic sample. Profiles resembling high-functioning, undercontrolled, and overcontrolled phenotypes were expected to emerge.MethodsWe used latent profile analysis on data from a sample of women with mental disorders (n = 313) and healthy controls (n = 114). 3-5 profile solutions were compared based on impulsivity, perfectionism, anxiety, stress susceptibility, mistrust, detachment, irritability, and embitterment. The best-fitting solution was then related to measures of depression, state anxiety, disordered eating, and emotion regulation difficulties to establish clinical significance.ResultsA 5-profile solution proved best-fitting. Extracted profiles included a high-functioning, a well-adapted, an impulsive and interpersonally dysregulated, an anxious and perfectionistic, and an emotionally and behaviorally dysregulated class. Significant differences were found in all outcome state measures, with the emotionally and behaviorally dysregulated class exhibiting the most severe psychopathology.DiscussionThese results serve as preliminary evidence of the predictive nature and clinical utility of personality-based profiles. Selected personality traits should be considered in case formulation and treatment planning. Further research is warranted to replicate the profiles and assess classification stability and profiles' association with treatment outcome longitudinally.
| S-EPMC9990091 | biostudies-literature
Project description:BackgroundIdentifying mechanisms of major depressive disorder that continue into remission is critical, as these mechanisms may contribute to subsequent depressive episodes. Biobehavioral markers related to depressogenic self-referential processing biases have been identified in adults with depression. Thus, we investigated whether these risk factors persisted during remission as well as contributed to the occurrence of stress and depressive symptoms over time.MethodsAt baseline, adults with remitted depression (n = 33) and healthy control subjects (n = 33) were administered diagnostic and stress interviews as well as self-report symptom measures. In addition, participants completed a self-referential encoding task while electroencephalography data were acquired. Stress interviews and self-report symptom measures were readministered at the 6-month follow-up assessment.ResultsDrift diffusion modeling showed that compared with healthy individuals, adults with remitted depression exhibited a slower drift rate to negative stimuli, indicating a slower tendency to reject negative stimuli as self-relevant. At the 6-month follow-up assessment, a slower drift rate to negative stimuli predicted greater interpersonal stress severity among individuals with remitted depression but not healthy individuals while controlling for both baseline depression symptoms and interpersonal stress severity. Highlighting the specificity of this effect, results were nonsignificant when predicting noninterpersonal stress. For self-relevant positive words endorsed, adults with remitted depression exhibited smaller left- than right-hemisphere late positive potential amplitudes; healthy control subjects did not show hemispheric differences.ConclusionsSelf-referential processing deficits persist into remission. In line with the stress generation framework, these biases predicted the occurrence of interpersonal stress, which may provide insight about a potential pathway for the re-emergence of depressive symptoms.
| S-EPMC9874080 | biostudies-literature
Project description:Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchical cluster analysis, but these computational approaches disregard the heterogeneous composition of individual cancer samples. Here, we used a more sophisticated unsupervised Bayesian model termed latent process decomposition (LPD), which handles individual cancer sample heterogeneity and deconvolutes the structure of transcriptome data to provide clinically relevant information. The work was performed on the pediatric tumor osteosarcoma, which is a prototypical model for a rare and heterogeneous cancer. The LPD model detected three osteosarcoma subtypes. The subtype with the poorest prognosis was validated using independent patient datasets. This new stratification framework will be important for more accurate diagnostic labeling, expediting precision medicine, and improving clinical trial success. Our results emphasize the importance of using more sophisticated machine learning approaches (and for teaching deep learning and artificial intelligence) for RNA-seq data analysis, which may assist drug targeting and clinical management.
| S-EPMC11658815 | biostudies-literature
Project description:BackgroundAdvancements in the treatment of depression are pivotal due to high levels of non-response and relapse. This study evaluated the role of personality pathology in the treatment of depression by testing whether maladaptive personality traits (1) predict changes in depression over treatment or vice versa, (2) change themselves over treatment, (3) change differentially depending on treatment with schema therapy (ST) or cognitive behavioural therapy (CBT), and (4) moderate the effectiveness of these treatments.MethodsWe included 193 depressed inpatients (53.4% women, Mage = 42.9, SD = 13.4) participating in an assessor-blind randomized clinical trial and receiving a 7-week course of ST or CBT. The research questions were addressed using multiple indicator latent change score models as well as multigroup structural equation models implemented in EffectLiteR.ResultsMaladaptive traits did not predict changes in depressive symptoms at post-treatment, or vice versa. However, maladaptive trait domains decreased over treatment (standardized Δμ range: -0.38 to -0.89), irrespective of treatment with ST or CBT. Maladaptive traits at baseline did not moderate the effectiveness of these treatments.ConclusionsSelf-reported maladaptive personality traits can change during treatment of depression, but may have limited prognostic or prescriptive value, at least in the context of ST or CBT. These results need to be replicated using follow-up data, larger and more diverse samples, and informant-rated measures of personality pathology.
| S-EPMC10388330 | biostudies-literature
Project description:Schema therapy (ST) is a relatively new, but promising, psychotherapy approach. Able to be implemented in both individual and group settings, research findings suggest that ST is a highly effective treatment for personality disorders. As in other treatments for personality disorders, some patients decide to drop out from treatment, feeling they did not benefit. To date, there has been no study in the literature that investigates the dropout rates across ST studies specifically. Consequently, this study systematically researched eight different ST studies in which dropout rates were reported. Together, these studies featured both individual and group therapy settings, inpatient and outpatient settings, and different personality disorder diagnoses. The weighted mean dropout rate was 23.3%, 95% CI (14.8-31.7%) across these studies. Although this finding is very similar to those meta-analyses that obtained their dropout rates from different orientations and diagnoses, namely psychotherapy in general, ST's dropout rates might be significantly lower than studies that included personality disorders in particular.
| S-EPMC7451381 | biostudies-literature
Project description:Objectives: The pathogenesis of heterogeneity in gastric cancer (GC) is not clear and presents as a significant obstacle in providing effective drug treatment. We aimed to identify subtypes of GC and explore the underlying pathogenesis. Methods: We collected two microarray datasets from GEO (GSE84433 and GSE84426), performed an unsupervised cluster analysis based on gene expression patterns, and identified related immune and stromal cells. Then, we explored the possible molecular mechanisms of each subtype by functional enrichment analysis and identified related hub genes. Results: First, we identified three clusters of GC by unsupervised hierarchical clustering, with average silhouette width of 0.96, and also identified their related representative genes and immune cells. We validated our findings using dataset GSE84426. Subtypes associated with the highest mortality (subtype 2 in the training group and subtype C in the validation group) showed high expression of SPARC, COL3A1, and CCN. Both subtypes also showed high infiltration of fibroblasts, endothelial cells, hematopoietic stem cells, and a high stromal score. Furthermore, subtypes with the best prognosis (subtype 3 in the training group and subtype A in the validation group) showed high expression of FGL2, DLGAP1-AS5, and so on. Both subtypes also showed high infiltration of CD4+ T cells, CD8+ T cells, NK cells, pDC, macrophages, and CD4+ T effector memory cells. Conclusion: We found that GC can be classified into three subtypes based on gene expression patterns and cell composition. Findings of this study help us better understand the tumor microenvironment and immune milieu associated with heterogeneity in GC and provide practical information to guide personalized treatment.
| S-EPMC8264374 | biostudies-literature
Project description:BackgroundThe current study's goal was to examine the multivariate patterns of associations between schema modes and emotion regulation mechanisms in personality disorders. Schema modes are either integrated or dissociative states of mind, including intense emotional states, efforts to regulate emotions, or self-reflective evaluative thought processes. Exploring the multivariate patterns of a shared relationship between schema modes and emotion regulation strategies may lead to a better understanding of their associations and a deeper understanding of the latent personality profiles that organize their associations in a mixed personality disorder sample.MethodsPatients who have personality disorders (N = 263) filled out five different self-report questionnaires, out of which four measured adaptive and maladaptive emotion-regulation strategies (Cognitive Emotion Regulation Questionnaire, Difficulty of Emotion Regulation Scale, Five Factor Mindfulness Questionnaire, Self-Compassion Scale), and the fifth one assessed schema modes (Schema Mode Inventory). We conducted canonical correlation analysis in order to measure the multivariate patterns of associations between the 26 emotion regulation and the 14 schema mode subscales.ResultsWe found strong multivariate associations between schema modes and emotion regulation strategies. Collectively, the full model based on all canonical variate pairs was statistically significant using the Wilks's Λ = .01 criterion, F (364,2804.4) = 3.5, p < .001. The first two canonical variate pairs yielded interpretable squared canonical correlation (Rc2) effect sizes of 74.7% and 55.8%, respectively. The first canonical variate pair represents a general personality pathology variable with a stronger weight on internalization than externalization, and bipolarity in terms of adaptive vs. non-adaptive characteristics. We labeled this variate pair "Adaptive/Non-Adaptive." The second canonical variate pair, labeled "Externalizing", represents externalizing schema modes and emotion regulation strategies.ConclusionUsing a multivariate approach (CCA), we identified two independent patterns of multivariate associations between maladaptive schema modes and emotion regulation strategies. The Adaptive/Non-Adaptive general personality pathology profile and the Externalizing personality pathology profile may lead to a deeper understanding of personality disorders and help psychotherapists in their conceptualization in order to design the most appropriate interventions.
| S-EPMC8194201 | biostudies-literature
Project description:BackgroundExisting digital mental health interventions mainly focus on the symptoms of specific mental disorders, but do not focus on Maladaptive Personalities and Interpersonal Schemas (MPISs). As an initial step toward considering personalities and schemas in intervention programs, there is a need for the development of tools for measuring core personality traits and interpersonal schemas known to cause psychological discomfort among potential users of digital mental health interventions. Thus, the MPIS was developed.ObjectiveThe objectives of this study are to validate the MPIS by comparing 2 models of the MPIS factor structure and to understand the characteristics of the MPIS by assessing its correlations with other measures.MethodsData were collected from 234 participants who were using web-based community sites in South Korea, including university students, graduate students, working professionals, and homemakers. All the data were gathered through web-based surveys. Confirmatory factor analysis was used to compare a single-factor model with a 5-factor model. Reliability and correlation analyses with other scales were performed.ResultsThe results of confirmatory factor analysis indicated that the 5-factor model (χ2550=1278.1; Tucker-Lewis index=0.80; comparative fit index=0.81; and Root Mean Square Error of Approximation=0.07) was more suitable than the single-factor model (χ2560=2341.5; Tucker-Lewis index=0.52; comparative fit index=0.54; and Root Mean Square Error of Approximation=0.11) for measuring maladaptive personality traits and interpersonal relationship patterns. The internal consistency of each factor of the MPIS was good (Cronbach α=.71-.88), and the correlations with existing measures were statistically significant. The MPIS is a validated 35-item tool for measuring 5 essential personality traits and interpersonal schemas in adults aged 18-39 years.ConclusionsThis study introduced the MPIS, a concise and effective questionnaire capable of measuring maladaptive personality traits and interpersonal relationship schemas. Through analysis, the MPIS was shown to reliably assess these psychological constructs and validate them. Its web-based accessibility and reduced item count make it a valuable tool for mental health assessment. Future applications include its integration into digital mental health care services, allowing easy web-based administration and aiding in the classification of psychological therapy programs based on the obtained results.Trial registrationClinicalTrials.gov NCT05952063; https://www.clinicaltrials.gov/study/NCT05952063.
| S-EPMC10576229 | biostudies-literature
Project description:Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.
| S-EPMC9931629 | biostudies-literature