The influence of posttraumatic stress disorder and recurrent major depression on risk-taking propensity following trauma script exposure among patients with substance use disorders

Microbiota alteration of Chinese young male adults with high-status negative cognitive processing bias

Introduction

Evidence suggests that negative cognitive processing bias (NCPB) is a significant risk factor for depression. The microbiota-gut-brain axis has been proven to be a contributing factor to cognitive health and disease. However, the connection between microbiota and NCPB remains unknown. This study mainly sought to explore the key microbiota involved in NCPB and the possible pathways through which NCPB affects depressive symptoms.

Methods

Data in our studies were collected from 735 Chinese young adults through a cross-sectional survey. Fecal samples were collected from 35 young adults with different levels of NCPB (18 individuals were recruited as the high-status NCPB group, and another 17 individuals were matched as the low-status NCPB group) and 60 with different degrees of depressive symptoms (27 individuals were recruited into the depressive symptom group, as D group, and 33 individuals were matched into the control group, as C group) and analyzed by the 16S ribosomal RNA sequencing technique.

Results

As a result, the level of NCPB correlated with the degree of depressive symptoms as well as anxiety symptoms and sleep quality (p < 0.01). The β-diversity of microbiota in young adults was proven to be significantly different between the high-status NCPB and the low-status NCPB groups. There were several significantly increased bacteria taxa, including Dorea, Christensenellaceae, Christe -senellaceae_R_7_group, Ruminococcaceae_NK4A214_group, Eggerthellaceae, Family-XIII, Family_XIII_AD3011_group, Faecalibaculum, and Oscillibacter. They were mainly involved in pathways including short-chain fatty acid (SCFA) metabolism. Among these variable bacteria taxa, Faecalibaculum was found associated with both NCPB and depressive symptoms. Furthermore, five pathways turned out to be significantly altered in both the high-status NCPB group and the depressive symptom group, including butanoate metabolism, glyoxylate and dicarboxylate metabolism, propanoate metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, valine, leucine, and isoleucine degradation. These pathways were related to SCFA metabolism.

Discussion

Fecal microbiota is altered in Chinese young male adults with high status NCPB and may be involved in the biochemical progress that influences depressive symptoms.

State-independent and -dependent behavioral and neuroelectrophysiological characteristics during dynamic decision-making in patients with current and remitted depression

BACKGROUND

It is unclear whether the altered decision-making (DM) observed in patients with major depressive disorder (MDD) is neurophysiological and whether it improves with remission of depressive symptoms. The aim of this study was to identify developmental patterns of DM behavior, related cognitive characteristics, and electrophysiological abnormalities in patients with MDD across clinical stages.

METHODS

A sample of 48 first-episode MDD patients (FD group), 41 remitted MDD patients (RD group), and 43 healthy controls (HCs) completed psychometric assessments and performed the balloon analogue risk task (BART) while event-related potentials (ERPs) were recorded.

RESULTS

The RD group had lower depressiveness, self-blame, rumination, and catastrophizing tendencies, and higher mental resilience scores than the FD group, but retained significant differences from HCs. MDD patients showed a more conservative DM strategy than HCs, with no significant difference between the FD and RD groups. Compared to the FD group, the RD group had a smaller FRN for negative feedback and a trend toward a smaller P3 for positive feedback. Compared with HCs, the RD group had a smaller P3 during the positive feedback phase. FRN amplitude correlated positively with depression level and negatively with mental resilience.

LIMITATIONS

Because a comparative cross-section design was employed, longitudinal studies are needed to make causal inferences.

CONCLUSION

MDD patients presented a stable risk-avoidance bias in actively depressed and remission periods, consistent with a state-independent impairment pattern. Significantly reduced FRN amplitudes during remission indicated a state-dependent impairment pattern, and FRN amplitudes correlated with depression level. An abnormal feedback P3 component may be a state-independent characteristic that may become more pronounced with MDD progression.

Depression in post-traumatic stress disorder

Major depressive disorder (MDD) symptoms commonly occur after trauma-exposure, both alone and in combination with post-traumatic stress disorder (PTSD). This article reviews recent research on comorbidity between these disorders, including its implications for symptom severity and response to treatment. Despite considerable symptom overlap, the two disorders represent distinct constructs and depend, at least in part, on separate biological mechanisms. Both, however, are also clearly related to stress psychopathology. We recommend that more research focus specifically on the study of individual differences in symptom expression in order to identify distinct subgroups of individuals and develop targeted treatments. However, a barrier to this line of inquiry is the trend of excluding particular patients from clinical trials of new interventions based on symptom severity or comorbidity. Another obstacle is the overreliance on self-report measures in human research. We argue that developing computer-based behavioral measures in order to supplement self-report can help address this challenge. Furthermore, we propose that these measures can help tie findings from human and non-human animal research. A number of paradigms have been used to model MDD-and PTSD-like behavior in animals. These models remain valuable for understanding the biological basis of these disorders in humans and for identifying potential interventions, but they have been underused for the study of comorbidity. Although the interpretation of animal behavior remains a concern, we propose that this can also be overcome through the development of close human analogs to animal paradigms.

Highlighting psychological pain avoidance and decision-making bias as key predictors of suicide attempt in major depressive disorder-A novel investigative approach using machine learning

OBJECTIVE

Predicting suicide is notoriously difficult and complex, but a serious public health issue. An innovative approach utilizing machine learning (ML) that incorporates features of psychological mechanisms and decision-making characteristics related to suicidality could create an improved model for identifying suicide risk in patients with major depressive disorder (MDD).

METHOD

Forty-four patients with MDD and past suicide attempts (MDD_SA, N = 44); 48 patients with MDD but without past suicide attempts (MDD_NS, N = 48-42 of whom with suicide ideation [MDD_SI, N = 42]), and healthy controls (HCs, N = 51) completed seven psychometric assessments including the Three-dimensional Psychological Pain Scale (TDPPS), and one behavioral assessment, the Balloon Analogue Risk Task (BART). Descriptive statistics, group comparisons, logistic regressions, and ML were used to explore and compare the groups and generate predictors of suicidal acts.

RESULTS

MDD_SA and MDD_NS differed in TDPPS total score, pain arousal and avoidance subscale scores, suicidal ideation scores, and relevant decision-making indicators in BART. Logistic regression tests linked suicide attempts to psychological pain avoidance and a risk decision-making indicator. The resultant key ML model distinguished MDD_SA/MDD_NS with 88.2% accuracy. The model could also distinguish MDD_SA/MDD_SI with 81.25% accuracy. The ML model using hopelessness could classify MDD_SI/HC with 94.4% accuracy.

CONCLUSION

ML analyses showed that motivation to avoid intolerable psychological pain, coupled with impaired decision-making bias toward under-valuing life's worth are highly predictive of suicide attempts. Analyses also demonstrated that suicidal ideation and attempts differed in potential mechanisms, as suicidal ideation was more related to hopelessness. ML algorithms show useful promises as a predictive instrument.