Association between negative cognitive bias and depression: A symptom-level approach

Somatic and mental symptoms associated with dysglycaemia, diabetes-related complications and mental conditions in people with diabetes: Assessments in daily life using continuous glucose monitoring and ecological momentary assessment

AIM

To analyse the potential drivers (glucose level, complications, diabetes type, gender, age and mental health) of diabetes symptoms using continuous glucose monitoring (CGM) and ecological momentary assessment.

MATERIALS AND METHODS

Participants used a smartphone application to rate 25 diabetes symptoms in their daily lives over 8 days. These symptoms were grouped into four blocks so that each symptom was rated six times on 2 days (noon, afternoon and evening). The symptom ratings were associated with the glucose levels for the previous 2 hours, measured with CGM. Linear mixed-effects models were used, allowing for nested random effects and the conduct of N = 1 analysis of individual associations.

RESULTS

In total, 192 individuals with type 1 diabetes and 179 with type 2 diabetes completed 6380 app check-ins. Four symptoms showed a significant negative association with glucose values, indicating higher ratings at lower glucose (speech difficulties, P = .003; coordination problems, P = .00005; confusion, P = .049; and food cravings, P = .0003). Four symptoms showed a significant positive association with glucose values, indicating higher scores at higher glucose (thirst, P = .0001; urination, P = .0003; taste disturbances, P = .021; and itching, P = .0120). There were also significant positive associations between microangiopathy and eight symptoms. Elevated depression and diabetes distress were associated with higher symptom scores. N = 1 analysis showed highly idiosyncratic associations between symptom reports and glucose levels.

CONCLUSIONS

The N = 1 analysis facilitated the creation of personalized symptom profiles related to glucose levels with consideration of factors such as complications, gender, body mass index, depression and diabetes distress. This approach can enhance precision monitoring for diabetes symptoms in precision medicine.

Brain mechanisms of rumination and negative self-referential processing in adolescent depression

BACKGROUND

Depression is linked to cognitive biases towards more negative and less positive self-relevant information. Rumination, perseverative negative thinking about the past and the self, may contribute to these biases.

METHODS

159 adolescents (12-18 years), with a range of depression symptoms, completed the SRET during fMRI. Multiple regressions tested associations between conventional self-report and ecological momentary assessment (EMA) measured rumination, and neural and behavioral responses during a self-referent encoding task (SRET).

RESULTS

Higher rumination (conventional self-report and EMA) was associated with more negative and fewer positive words endorsed and recalled. Higher self-reported (but not EMA) rumination was associated with higher accuracy in recognizing negative words and greater insula and dorsal anterior cingulate activity to negative versus positive words.

LIMITATIONS

The sample included mostly non-Hispanic White participants with household incomes above the national average, highlighting the need for replication in more diverse samples. Word endorsement discrepancies required fMRI analyses to model neural response to viewing negative versus positive words.

CONCLUSIONS

Adolescents with higher rumination endorsed and recalled more negative and fewer positive words and recognized more negative words during the SRET. Higher insula reactivity, a key region for modulating externally-oriented attention and internally-oriented self-referential processes, may contribute to links between rumination and negative memory biases. These findings provide insight into neurocognitive mechanisms underlying depression.

Loneliness trajectories and psychological distress in youth: Longitudinal evidence from a population-based sample

This pre-registered secondary analysis aimed to examine distinct longitudinal loneliness trajectories in youth and whether these trajectories were associated with psychological distress at final follow-up in the UK Household Longitudinal Study. Participants (N = 827, 55.1% female, Time 1: M ± SD = 16.50 ± 0.50 years) provided data during Waves 9, 10 and 11. K-means longitudinal clustering analysis was used to identify clusters of participants with distinct loneliness trajectories across measurement waves. We identified four clusters demonstrating distinct trajectories of loneliness: stable low (40.7%), stable high (20.6%), moderate decreasing (19.6%) and low increasing (19.1%). Compared to 'stable low loneliness', 'stable high' and 'low increasing' loneliness clusters were significantly associated with psychological distress at Wave 11 following adjustment for sex, ethnicity, parent's highest educational achievement and Wave 9 psychological distress. The current study offers an important contribution to the literature on patterns of youth loneliness and mental health consequences.

Revisiting the role of computational neuroimaging in the era of integrative neuroscience

Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.

Tell me why: the missing w in episodic memory's what, where, and when

Endel Tulving defined episodic memory as consisting of a spatiotemporal context. It enables us to recollect personal experiences of people, things, places, and situations. In other words, it is made up of what, where, and when components. However, this definition does not include arguably the most important aspect of episodic memory: the why. Understanding why we remember has important implications to better understand how our memory system works and as a potential target of intervention for memory impairment. The intrinsic and extrinsic factors related to why some experiences are better remembered than others have been widely investigated but largely independently studied. How these factors interact with one another to drive an event to become a lasting memory is still unknown. This review summarizes research examining the why of episodic memory, where we aim to uncover the factors that drive core features of our memory. We discuss the concept of episodic memory examining the what, where, and when, and how the why is essential to each of these key components of episodic memory. Furthermore, we discuss the neural mechanisms known to support our rich episodic memories and how a why signal may provide critical modulatory impact on neural activity and communication. Finally, we discuss the individual differences that may further drive why we remember certain experiences over others. A better understanding of these elements, and how we experience memory in daily life, can elucidate why we remember what we remember, providing important insight into the overarching goal of our memory system.

Effects of activity participation and cognitive levels on depression in middle-aged and older adults with chronic illness: a national cross-sectional study

Introduction

The world population is rapidly aging, and depression mainly affects middle-aged and older adults with chronic diseases and cognitive impairments. The sample for this study was obtained from the China Health and Retirement Longitudinal Study (CHARLS) public database. The sample size for inclusion was 12,767. There were 6,773 females and 5,994 males, with an overall low level of education. This study aims to provide a theoretical and practical reference basis for the clinical non-pharmacological treatment of depression in middle-aged and older adults (age ≥ 50 years) with chronic diseases. Additionally, the study seeks to promote the development of mental health interventions for middle-aged and older adults (age ≥ 50 years) with chronic diseases, ultimately enhancing the sense of well-being and quality of life for this demographic.

Methods

Cognitive functioning and depressive symptoms of the study participants were assessed using the Mini-Mental State Examination Scale (MMSE) and the short version of the Center for Epidemiological Studies Depression Scale (CESD-10).

Results and discussion

Binary logistic regression results showed that among middle-aged and older adults (age ≥ 50 years) with chronic diseases, participation in physical activity [OR = 1.397; 95% CI (1.181-1.651); p < 0.05] was more effective than participation in social activities [OR = 0.997; 95% CI (0.924-1.076); p < 0.05] for preventing depression. Those with cognitive impairment [OR = 1.206; 95% CI (1.089-1.335); p < 0.05] were more likely to experience depression than those without cognitive impairment. Activity participation (physical activity and social activity) had a more significant effect on mild and moderate depression compared to no depression, and cognitive level had a more pronounced effect on moderate depression [OR = 1.491; 95% CI (1.278-1.740); p < 0.05] and major depression [OR = 2.231; 95% CI (1.282-3.884); p < 0.05]. Within the specific cohort of middle-aged and older adults (age ≥ 50 years) with chronic diseases, both activity participation and cognitive level exert a significant influence on the prevention and intervention of depression. Engagement in physical activity, participation in social activities, and enhanced cognitive functioning emerged as protective factors against depression. Therefore, the policy-maker should strengthen the prevention and treatment of depression in a comprehensive manner through the promotion of physical and social activities and the enhancement of cognitive level, so as to safeguard the mental health of middle-aged and older adults with chronic diseases.

Emotional reactivity and its impact on neural circuitry for attention-emotion interaction through regression-based machine learning model

Attentional paradigm can have a significant influence on the processing and experience of positive and negative emotions. Attentional mechanism refers to the tendency to selectively attend to a particular stimulus while ignoring others. In the context of emotions, individuals may exhibit attentional biases towards either positive or negative emotional stimuli. By directing attention towards a specific stimulus, individuals can modulate their emotional responses. When attention is directed towards negative or threatening stimuli, it can intensify negative emotions such as fear, sadness, anger and anxiety. Conversely, directing attention away from negative stimuli can reduce emotional reactivity and promote emotional regulation. Similarly, paying attention to positive stimuli can amplify positive emotions and facilitate positive emotional experiences. Attentional paradigms are also responsible for cognitive appraisal of emotional stimuli. The allocation of attention can shape how emotional stimuli are evaluated and categorized, influencing the subsequent emotional response. Since the relationship between attention and emotions is complex and can vary across individuals and contexts, it is important to understand the underlying cognitive neural dynamics of the same. Custom rank allocation model (CRAM) was used to decode the underlying neural dynamics of cognitive and emotional resource sharing through the non-significant EEG channels. During the main effect of global-local (GL), CRAM ranks and scores indicated that the EEG channels C4, PZ, OZ, and P4 were found to be the most non-significant channels. Similarly, CRAM ranks and scores of the interaction effects between global-local and positive emotion-negative emotion and the interaction effects between global-local and frequent-deviant-equal indicated midline central EEG channels CZ, PZ, FZ and OZ to be the main contributor of the cognitive and emotional resources to others. Understanding the dynamics of attention-emotion conflicts with reference to significant and non-significant channels is important to gain insights into the complex computational interplay between attention and emotion, leading to a deeper understanding of human cognition and emotion regulation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10106-z.

Negative self-referent cognition predicts future depression symptom change: an intensive sampling approach

Cognitive theories of depression assert that negative self-referent cognition has a causal role in the development and maintenance of depression symptoms, but few studies have examined temporal associations between these constructs using intensive, longitudinal sampling strategies. In three samples of undergraduate students, we examined associations between change in self-referent processing and depression across 5 daily assessments (Sample 1, N = 303, 1,194 measurements, 79% adherence), 7 daily assessments (Sample 2, N = 313, 1,784 measurements, 81% adherence), and 7 weekly assessments (Sample 3; N = 155, 833 measurements, 81% adherence). Random intercept cross-lagged panel models indicated large cross-lagged effects in two of the three samples (Samples 1 and 3 but not Sample 2), such that more negative self-referent thinking than usual was significantly associated with a subsequent increase in depression symptoms at the next time lag. Notably, change in depression from usual was not associated with increases in negative self-referent processing at the next time point in any sample. These findings suggest that change in negative self-referent processing may be causally linked to future increases in depression on a day-to-day and week-to-week basis, although confidence in this conclusion is tempered somewhat by a lack of replication in Sample 2.

Effects of attentional deployment training for relieving negative emotion in individuals with subthreshold depression

OBJECTIVE

As a prodromal stage to major depressive disorder (MDD), subthreshold depression (StD) has a higher prevalence in the population, resulting in a greater healthcare burden. StD individuals' current negative emotion could be moderated by attentional deployment. However, it remains unclear whether attentional deployment training can mitigate subsequent negative emotion in StD individuals.

METHODS

Based on 160 participants, we combined decision task (Experiment 1, N = 69), eye-tracking (Experiment 2, N = 40), and EEG (Experiment 3, N = 51) techniques to investigate how one-week attentional deployment (gain-focus, GF) training modulated the emotional processing of negative stimulus and its underlying neural correlates in StD individuals.

RESULTS

After one-week GF training, StD individuals significantly reduced the first fixation time and total fixation time on the negative part (missed opportunities) of decision outcome and showed a decrease in emotional sensitivity to missed opportunities. An increase in N1 and decrease in P3 and LPP (late positive potentials) amplitudes, as well as a decrease in alpha oscillation, were observed when StD individuals faced missed opportunities after training. Additionally, the extent of reduction in StD individuals' emotional sensitivity to missed opportunities could be significantly predicted by the degree of decrease in alpha oscillation.

CONCLUSION

One-week attentional deployment training could modulate negative emotion in StD individuals and the degree of change in alpha oscillation might act as an objective indicator for the effectiveness of training.

SIGNIFICANCE

Our study provides a convenient and effective approach to alleviate the negative emotion of StD individuals.

Increased Odds of Cognitive Impairment in Adults with Depressive Symptoms and Antidepressant Use

INTRODUCTION

The relationship between antidepressant use and class with cognition in depression is unclear. This study aimed to evaluate the association of cognition with depressive symptoms and antidepressant use (class, duration, number).

METHODS

Data from the National Health and Nutrition Examination Survey were examined for cognitive function through various tests and memory issues through the Medical Conditions questionnaire. Depressive symptoms were assessed using the Patient Health Questionnaire-9.

RESULTS

A total of 2867 participants were included. Participants with depressive symptoms had significantly higher odds of cognitive impairment (CI) on the animal fluency test (aOR=1.89, 95% CI=1.30, 2.73, P=0.002) and Digit Symbol Substitution test (aOR=2.58, 95% CI=1.34, 4.9, P=0.007), as well as subjective memory issues (aOR=7.25, 95% CI=4.26, 12.32, P<0.001) than those without depression. There were no statistically significant associations between any of the CI categories and depressive symptoms treated with an antidepressant and antidepressant use duration. Participants who were using more than one antidepressant had significantly higher odds of subjective memory issues than those who were using one antidepressant. Specifically, users of atypical antidepressants, selective serotonin reuptake inhibitors, or tricyclic antidepressants (TCAs) had significantly higher odds of subjective memory issues in comparison to no antidepressants, with TCAs showing the largest odds (aOR=4.21, 95% CI=1.19, 14.86, P=0.028).

DISCUSSION

This study highlights the relationship between depressive symptoms, antidepressant use, and CI. Future studies should further evaluate the mechanism underlying this phenomenon.

The effects of shared, depression-specific, and anxiety-specific internalizing symptoms on negative and neutral episodic memories following post-learning sleep

Emotional memory bias is a common characteristic of internalizing symptomatology and is enhanced during sleep. The current study employs bifactor S-1 modeling to disentangle depression-specific anhedonia, anxiety-specific anxious arousal, and the common internalizing factor, general distress, and test whether these internalizing symptoms interact with sleep to influence memory for emotional and neutral information. Healthy adults (N = 281) encoded scenes featuring either negative objects (e.g., a vicious looking snake) or neutral objects (e.g., a chipmunk) placed on neutral backgrounds (e.g., an outdoor scene). After a 12-hour period of daytime wakefulness (n = 140) or nocturnal sleep (n = 141), participants judged whether objects and backgrounds were the same, similar, or new compared with what they viewed during encoding. Participants also completed the mini version of the Mood and Anxiety Symptom Questionnaire. Higher anxious arousal predicted worse memory across all stimuli features, but only after a day spent being awake-not following a night of sleep. No significant effects were found for general distress and anhedonia in either the sleep or wake condition. In this study, internalizing symptoms were not associated with enhanced emotional memory. Instead, memory performance specifically in individuals with higher anxious arousal was impaired overall, regardless of emotional valence, but this was only the case when the retention interval spanned wakefulness (i.e., not when it spanned sleep). This suggests that sleep may confer a protective effect on general memory impairments associated with anxiety.

Community-engaged artificial intelligence research: A scoping review

The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

Psychometric Properties of a Novel Affective Bias Task and Its Application in Clinical and Nonclinical Populations

To mitigate limitations of self-reported mood assessments, we introduce a novel affective bias task. The task quantifies instantaneous emotional state by leveraging the phenomenon of affective bias, in which people interpret external emotional stimuli in a manner consistent with their current emotional state. This study establishes task stability in measuring and tracking depressive symptoms in clinical and nonclinical populations. Initial assessment in a large nonclinical sample established normative ratings. Depressive symptoms were measured and compared with task performance in a nonclinical sample, as well as in a clinical cohort of individuals who were undergoing surgical evaluation for severe epilepsy. In both cohorts, a stronger negative affective bias was associated with a higher Beck Depression Inventory-II score. The affective bias task exhibited high stability and interrater reliability as well as construct validity in predicting depression levels in both cohorts, suggesting that the task is a reliable proxy for mood and a diagnostic tool for detecting depressive symptoms.

Heterogeneity in the co-occurrence of depression and anxiety among adolescents: Results of latent profile analysis

BACKGROUND

Depression and anxiety co-occur frequently and there is heterogeneity in the co-occurrence of such symptoms; however, few previous studies investigated the heterogeneity based on person-centered perspectives in adolescents. The primary aim of our study was to explore it using latent profile analysis (LPA), a person-centered statistical approach.

METHOD

The Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) were used to examine depression and anxiety symptoms in 7422 Chinese adolescents from 23 primary and secondary schools. To investigate latent profiles and assess profile validity, we employed Latent Profile Analysis (LPA), multinomial logistic regression, and analysis of variance.

RESULTS

A three-profile model was suggested as the optimum: low (69.9 %), moderate (21.6 %), and high depression/anxiety (8.5 %). Female with higher negative cognitive bias and higher emotional regulation difficulty are more likely to be categorized in the high depression/anxiety group. Internet addiction, academic "Lying flat" and involution are significantly and positively linked with the severity of anxiety and depression.

LIMITATIONS

Reliance on self-reported measures may lead to response bias; the cross-sectional design limits our ability to study how symptom profiles and category membership change over time.

CONCLUSIONS

Three latent profiles of the co-occurrence of depression and anxiety presented a parallel pattern, which serves as a poignant reminder of the imperative need to identify Chinese adolescents who may be at elevated risk for depression and/or anxiety, and promoting intervention that are meticulously tailored to address the unique symptom presentations of each individual.

Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework

Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis, are efficacious; however, mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support for explaining symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in cognitive behavioral therapy for psychosis-based psychotherapy for persecutory delusions, which targets specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective cognitive behavioral therapy for psychosis systematically targets the way sensory data are selected, experienced, and interpreted, thus allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.

Effectiveness of Mindfulness-Based Cognitive Therapy on Depressive Symptoms, Brain Potential, and Neuroimmunoinflammatory Factors in Depressed Patients

OBJECTIVE

This study was aimed to investigate the effectiveness of mindfulness-based cognitive therapy (MBCT) on depressive symptoms, brain potential, and neuroimmunoinflammatory factors in patients with depression.

METHODS

Sixty-four eligible patients according to the inclusion criteria were randomly divided into the control group and the observation group, with 32 patients in each group. The control group received conventional therapy, while the observation group received MBCT on top of conventional therapy. The depressive symptoms, brain potential, and neuroimmunoinflammatory factors were measured in the two groups.

RESULTS

After treatment, the Hamilton Depression Rating Scale score, tumor necrosis factor α, and interleukin-6 levels were decreased, while the World Health Organization Quality of Life Scale score, total number of response execution score, and 5-hydroxy tryptamine level were increased in both groups. Moreover, the Hamilton Depression Rating Scale score, tumor necrosis factor α, and interleukin-6 levels were decreased more significantly, while the World Health Organization Quality of Life Scale score, total number of response execution score, and 5-hydroxy tryptamine level were increased more significantly in the observation group compare to the control group ( P < 0.01). In addition, the latency in the observation group was shorter and the amplitude was longer than those in the control group ( P < 0.01).

CONCLUSIONS

Compared with conventional therapy, the use of MBCT combined with conventional therapy can effectively reduce depressive symptoms, suppresses inflammatory responses, and optimize attention and response to target stimulation and is worthy of wide clinical implementation.

Disorder-specific versus transdiagnostic cognitive mechanisms in anxiety and depression: Machine-learning-based prediction of symptom severity

INTRODUCTION

Psychiatric evaluation of anxiety and depression is currently based on self-reported symptoms and their classification into discrete disorders. Yet the substantial overlap between these disorders as well as their within-disorder heterogeneity may contribute to the mediocre success rates of treatments. The proposed research examines a new framework for diagnosis that is based on alterations in underlying cognitive mechanisms. In line with the Research Domain Criteria (RDoC) approach, the current study directly compares disorder-specific and transdiagnostic cognitive patterns in predicting the severity of anxiety and depression symptoms.

METHODS

The sample included 237 individuals exhibiting differing levels of anxiety and depression symptoms, as measured by the STAI-T and BDI-II. Random Forest regressors were used to analyze their performance on a battery of six computerized cognitive-behavioral tests targeting selective and spatial attention, expectancy, interpretation, memory, and cognitive control biases.

RESULTS

Unique anxiety-specific biases were found, as well as shared anxious-depressed bias patterns. These cognitive biases exhibited relatively high fitting rates when predicting symptom severity (questionnaire scores common range 0-60, MAE = 6.03, RMSE = 7.53). Interpretation and expectancy biases exhibited the highest association with symptoms, above all other individual biases.

LIMITATIONS

Although internal validation methods were applied, models may suffer from potential overfitting due to sample size limitations.

CONCLUSION

In the context of the ongoing dispute regarding symptom-centered versus transdiagnostic approaches, the current study provides a unique comparison of these two views, yielding a novel intermediate approach. The results support the use of mechanism-based dimensional diagnosis for adding precision and objectivity to future psychiatric evaluations.

Personality traits and depressive symptoms among Chinese older people: A network approach

BACKGROUND

The present study aims to investigate the network structure of depressive symptoms, the interrelationships between individual personality traits and depressive symptoms, and gender differences among Chinese older people aged 60 and above.

METHOD

We performed network analyses with a regularized Graphical Gaussian Model and a case-dropping bootstrap approach. A sample of 4876 older Chinese people aged 60+ was included in the analyses. We investigated the central symptoms in the depression network and the bridge nodes that connect personality facets and depressive symptoms. Gender differences were investigated by testing the global strength, network invariance, and edge weights.

RESULTS

Sadness and depressed mood were the most central depressive symptoms, while somatic symptoms such as restless sleep were the least central. Neurotic facets, particularly "worry a lot" and "get nervous easily", played significant bridging roles in the web of personality traits and depressive symptoms. Gender differences were observed in three edges among different personality traits (rude-worried, original-worried, and forgiving-nervous).

LIMITATION

The study adopts a cross-sectional dataset, and therefore, cannot track the network changes over time or conclude a causal relationship.

DISCUSSION

The study calls for more focus and prioritization on sadness, depressed mood and neurotic traits in the identification of depression among older Chinese people. Future researchers and practitioners should better understand of older Chinese adults' worry and nervousness to develop appropriate practices and policies.

Transdiagnostic cognitive biases in psychiatric disorders: A systematic review and network meta-analysis

Psychiatric disorders are characterized by cognitive deficits, which have been proposed as a transdiagnostic feature of psychopathology ("C" factor). Similarly, cognitive biases (e.g., in attention, memory, and interpretation) represent common tendencies in information processing that are often associated with psychiatric symptoms. However, the question remains whether cognitive biases are also transdiagnostic or are specific to certain psychiatric disorders/symptoms. The current systematic review sought to address whether the proposed "C" factor of transdiagnostic cognitive dysfunction in psychopathology can be extended to cognitive biases. Overall, 31 studies comprising 4401 participants (2536 patients, 1865 non-clinical controls) met inclusion criteria, assessing 19 cognitive biases across 20 diagnostic categories, with most studies focusing on interpretation (k = 22) and attention (k = 11) biases and only 2 assessing memory biases. Traditional meta-analyses found a moderate effect size (g = 0.32) for more severe cognitive biases in all patients relative to non-clinical controls, as well as small but significant associations between interpretation biases and transdiagnostic symptom categories (general psychopathology: r = 0.20, emotion dysfunction: r = 0.17, psychotic symptoms: r = 0.25). Network meta-analyses revealed significant patient versus non-clinical control differences on attention and interpretation biases across diagnoses, as well as significant differences between diagnoses, with highest severity in panic disorder for attention biases and obsessive-compulsive disorder for interpretation biases. The current findings extend the big "C" interpretation of transdiagnostic cognitive dysfunction in psychiatric disorders to cognitive biases and transdiagnostic symptom dimensions. Results also suggest that while the presence of cognitive biases is transdiagnostic, bias severity differs across diagnoses, as in traditional neurocognitive deficits.

Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression

Introduction

The heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders.

Methods

In this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category.

Results

We identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled "Neurotic" (C1), "Extraverted" (C2), "Anxious to please" (C3), "Self-critical" (C4), "Conscientious" (C5). The non-clinical clusters were labeled "Self-confident" (N1), "Low endorsement" (N2), "Non-neurotic" (N3), "Neurotic" (N4), "High endorsement" (N5). The combined clusters were labeled "Self-confident" (NC1), "Externalising" (NC2), "Neurotic" (NC3), "Secure" (NC4), "Low endorsement" (NC5), "High endorsement" (NC6), "Self-critical" (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism.

Discussion

Overall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.

A quantitative textual analysis of the subjective effects of ayahuasca in naïve users with and without depression

Ayahuasca is a brew with psychoactive properties that has been used as an entheogen for centuries, with more recent studies suggesting it is a promising treatment for some clinical disorders. Although there is an emerging scientific literature on its effects, to the best of our knowledge no study has explored the self-reported experiences of first-time ayahuasca users with quantitative textual analysis tools. Accordingly, the current study aimed to analyze the subjective experience of naive individuals with depression and healthy controls after consuming ayahuasca. For this purpose, responses from a subsample of participants from a previous clinical trial to open-ended questions regarding their experience with ayahuasca underwent textual analysis. Data from nine patients with treatment-resistant depression and 20 healthy individuals were included, and quantitative textual analysis was performed using IRaMuTeQ 0.7 alpha 2 and R 3.1.2. The analysis identified five clusters: alterations in the state of consciousness, cognitive changes, somatic alterations, auditory experiences, and visual perceptual content. Additionally, findings suggest specific features of the experience of people with depression with ayahuasca, such as increased aversive bodily reactions. The results are consistent with previous findings indicating central axes of the psychedelic experience, and may inform therapeutic approaches using ayahuasca.

Catch the drift: Depressive symptoms track neural response during more efficient decision-making for negative self-referents

Background

Adolescence is a time of heightened risk for developing depression and also a critical period for the development and integration of self-identity. Despite this, the relation between the neurophysiological correlates of self-referential processing and major depressive symptoms in youth is not well understood. Here, we leverage computational modeling of the self-referential encoding task (SRET) to identify behavioral moderators of the association between the posterior late positive potential (LPP), an event-related potential associated with emotion regulation, and youth self-reported symptoms of depression. Specifically, within a drift-diffusion framework, we evaluated whether the association between the posterior LPP and youth symptoms of major depression was moderated by drift rate, a parameter reflecting processing efficiency during self-evaluative decisions.

Methods

A sample of 106 adolescents, aged 12 to 17 (53% male; Mage = 14.49, SD = 1.70), completed the SRET with concurrent high-density electroencephalography and self-report measures of depression and anxiety.

Results

Findings indicated a significant moderation: for youth showing greater processing efficiency (drift rate) when responding to negative compared to positive words, larger posterior LPPs predicted greater depressive symptom severity.

Limitations

We relied on a community sample and our study was cross-sectional in nature. Future longitudinal work with clinically depressed youth would be beneficial.

Conclusions

Our results suggest a neurobehavioral model of adolescent depression wherein efficient processing of negative information co-occurs with increased demands on affective self-regulation. Our findings also have clinical relevance; youth's neurophysiological response (posterior LPP) and performance during the SRET may serve as a novel target for tracking treatment-related changes in one's self-identity.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

Electroconvulsive Therapy: Mechanisms of Action, Clinical Considerations, and Future Directions

LEARNING OBJECTIVES

• Outline and discuss the fundamental physiologic, cellular, and molecular mechanisms of ECT to devise strategies to optimize therapeutic outcomes• Summarize the overview of ECT, its efficacy in treating depression, the known effects on cognition, evidence of mechanisms, and future directions.

ABSTRACT

Electroconvulsive therapy (ECT) is the most effective treatment for a variety of psychiatric illnesses, including treatment-resistant depression, bipolar depression, mania, catatonia, and clozapine-resistant schizophrenia. ECT is a medical and psychiatric procedure whereby electrical current is delivered to the brain under general anesthesia to induce a generalized seizure. ECT has evolved a great deal since the 1930s. Though it has been optimized for safety and to reduce adverse effects on cognition, issues persist. There is a need to understand fundamental physiologic, cellular, and molecular mechanisms of ECT to devise strategies to optimize therapeutic outcomes. Clinical trials that set out to adjust parameters, electrode placement, adjunctive medications, and patient selection are critical steps towards the goal of improving outcomes with ECT. This narrative review provides an overview of ECT, its efficacy in treating depression, its known effects on cognition, evidence of its mechanisms, and future directions.

Integrating cognitive bias modification for pain and opioid cues into medication for opioid use disorder clinical care: Feasibility, acceptability, and preliminary results

BACKGROUND

Despite high co-occurrence, chronic pain is often unaddressed in treatment for opioid use disorder (OUD) and little is known about mechanisms that may underlie associations between pain and opioid use. Using an attentional bias (AB) task with both pain and opioid cues, we evaluated a cognitive bias modification (CBM) task administered during regularly scheduled medications for OUD (mOUD) dosing visits. The current study evaluated the feasibility, acceptability, and preliminary efficacy of the CBM task. Outcomes for AB tasks used traditional mean-based score and trial-level bias scores (TLBS).

METHODS

In a double-blind, randomized controlled trial, 28 individuals with OUD and chronic pain engaged in mOUD were randomized to either CBM or an AB control condition and completed up to three tasks per week for four weeks. Standard AB task was completed at baseline and post-treatment. Participants completed feasibility and acceptability measures, and preliminary efficacy (i.e., change in AB) was assessed using ANOVA models.

RESULTS

Participants attended 83.3% of scheduled sessions and generally reported the task was enjoyable, credible, and easy to complete. Preliminary results demonstrated a condition by time interaction highlighting a reduction in AB in the CBM group but not the control group in opioid TLBS variability (F[1,26]=5.01, p = .034) and pain TLBS towards (F[1,26]=6.42, p = .018) and pain TLBS variability (F[1,26]=5.24, p = .03).

CONCLUSIONS

The current study supports integrating brief, computer-based tasks designed to reduce AB into mOUD clinical care. The preliminary results suggest that TLBS outcomes may be more sensitive to capture changes in AB; however, larger studies are required.

Associating broad and clinically defined polygenic scores for depression with depression-related phenotypes

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank in an independent sample of adults (N = 210; 100% European Ancestry) who were extensively phenotyped for depression and related neurocognitive traits (e.g., rumination, emotion regulation, anhedonia, and resting frontal alpha asymmetry). The UK Biobank-derived PGSBD had small associations with MDD, depression severity, anhedonia, cognitive reappraisal, brooding, and suicidal ideation but only the association with suicidal ideation remained statistically significant after correcting for multiple comparisons. Similarly small associations were observed for the PGSMDD but none remained significant after correcting for multiple comparisons. These findings provide important initial guidance about the expected effect sizes between current UKB PGSs for depression and depression-related neurocognitive phenotypes.

The Structure of Compulsive Sexual Behavior: A Network Analysis Study

Compulsive sexual behavior is a phenomenon characterized by a persistent failure to control intense, repetitive sexual impulses or urges, resulting in repetitive sexual behavior that causes marked distress or impairment in personal, familial, social, educational, or occupational areas of functioning. Despite its major impact on mental health and quality of life, little is known about its internal structure and whether this phenomenon differs across genders, age groups, and risk status. By considering a large online sample (n = 3186; 68.3% males), ranging from 14 to 64 years old, compulsive sexual behavior was explored by means of network analysis. State-of-the-art analytical techniques were adopted to investigate the pattern of association among the different elements of compulsive sexual behavior, identify possible communities of nodes, pinpoint the most central nodes, and detect differences between males and females, among different age groups, as well as between individuals at low and high risk of developing a full-blown disorder. The analyses revealed that the network was characterized by three communities, namely Consequence, Preoccupation, and Perceived Dyscontrol, and that the most central node was related to (perceived) impulse dyscontrol. No substantial differences were found between males and females and across age. Failing to meet one's own commitments and responsibilities was more central in individuals at high risk of developing a full-blown disorder than in those at low risk.

Computational Modeling of Self-Referential Processing Reveals Domain General Associations with Adolescent Anxiety Symptoms

What an adolescent thinks about themselves, commonly termed self-referential processing, has significant implications for youth long-term psychological well-being. Self-referential processing plays an important role in anticipatory and reactive processing in social contexts and contributes to symptoms of social anxiety. Previous work examining self-referential processing largely focuses on child and adolescent depression, relying on endorsement and reaction time for positive and negative self-describing adjectives in a self-referential encoding task (SRET). Here, we employ computational methods to interrogate the latent processes underlying choice reaction times to evaluate the fit of several drift-diffusion models of youth SRET performance. A sample of 106 adolescent, aged 12-17 (53% male; M_age = 14.49, SD = 1.70) completed the SRET and self-report measures of anxiety and depression. Our results support the utility of modeling the SRET, where the rate of evidence accumulation (i.e., drift rate) during negative self-referential processing was related to social anxiety above-and-beyond mean task performance. Our regression analyses indicated that youth efficiency in processing of self-referential views was domain general to anxiety, highlighting the importance of assessing both social and physiological anxiety symptoms when predicting SRET performance. The computational modeling results revealed that self-referential views are not uniquely related to depression-related constructs but also facets of anxiety.

Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms

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.

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.

Trial-level bias score versus mean bias score: Comparison of the reliability and external validity using dot-probe task among daily smokers

INTRODUCTION

Attentional bias (AB) is an individual difference risk factor that represents the extent to which cigarette cues capture one's attention. AB is typically indexed by mean bias score (MBS), theoretically assuming that AB is static. However, poor reliability of MBS has threatened valid interpretation of the results on AB. Based on observed trial-by-trial temporal fluctuation and variability of attentional allocation, trial-level bias score (TLBS) has been introduced as an alternative index with evidence of better psychometric properties in various populations, as compared to MBS. However, such evidence is limited among daily smokers. The current study aimed to replicate and extend extant findings in a sample of daily smokers by hypothesizing that TLBS, as compared to MBS, would demonstrate superior reliability and external validity.

METHODS

Forty-eight daily smokers completed self-reports, ad-libitum smoking, and a dot-probe task three times, which was comprised of 36 pairs of pictorial stimuli of cigarette and neutral cues, yielding 144 total trials.

RESULTS

The TLBS demonstrated superior internal (range intra class correlation [ICC] = 0.79-0.95) and test-retest reliability (range ICC = 0.64-0.88) compared to MBS (range ICC = 0.31-0.40 and 0.06-0.16, respectively). However, few significant relations between either the MBS or TLBS and measures of biobehavioral and self-report indices of smoking reinforcement were observed.

CONCLUSIONS

The current findings demonstrate that TLBS, as compared to MBS, is a more reliable measure of AB among daily smokers, while evidence of its external validity is limited.

The importance of a multidimensional approach to the preclinical study of major depressive disorder and apathy

Both the neuropsychiatric syndrome of apathy and major depressive disorder comprise a heterogenous cluster of symptoms which span multiple behavioural domains. Despite this heterogeneity, there is a tendency in the preclinical literature to conclude a MDD or apathy-like phenotype from a single dimensional behavioural task used in isolation, which may lead to inaccurate phenotypic interpretation. This is significant, as apathy and major depressive disorder are clinically distinct with different underlying mechanisms and treatment approaches. At the clinical level, apathy and major depressive disorder can be dissociated in the negative valence (loss) domain of the Research Domain Criteria. Symptoms of MDD in the negative valence (loss) domain can include an exaggerated response to emotionally salient stimuli and low mood, while in contrast apathy is characterised by an emotionally blunted state. In this article, we highlight how using a single dimensional approach can limit psychiatric model interpretation. We discuss how integrating behavioural findings from both the positive and negative (loss) valence domains of the Research Domain Criteria can benefit interpretation of findings. We focus particularly on behaviours relating to the negative valence (loss) domain, which may be used to distinguish between apathy and major depressive disorder at the preclinical level. Finally, we consider how future approaches using home cage monitoring may offer a new opportunity to detect distinct behavioural profiles and benefit the overall translatability of findings.

Sex differences in inflammation in the hippocampus and amygdala across the lifespan in rats: associations with cognitive bias

Background

Cognitive symptoms of major depressive disorder, such as negative cognitive bias, are more prevalent in women than in men. Cognitive bias involves pattern separation which requires hippocampal neurogenesis and is modulated by inflammation in the brain. Previously, we found sex differences in the activation of the amygdala and the hippocampus in response to negative cognitive bias in rats that varied with age. Given the association of cognitive bias to neurogenesis and inflammation, we examined associations between cognitive bias, neurogenesis in the hippocampus, and cytokine and chemokine levels in the ventral hippocampus (HPC) and basolateral amygdala (BLA) of male and female rats across the lifespan.

Results

After cognitive bias testing, males had more IFN-γ, IL-1β, IL-4, IL-5, and IL-10 in the ventral HPC than females in adolescence. In young adulthood, females had more IFN-γ, IL-1β, IL-6, and IL-10 in the BLA than males. Middle-aged rats had more IL-13, TNF-α, and CXCL1 in both regions than younger groups. Adolescent male rats had higher hippocampal neurogenesis than adolescent females after cognitive bias testing and young rats that underwent cognitive bias testing had higher levels of hippocampal neurogenesis than controls. Neurogenesis in the dorsal hippocampus was negatively associated with negative cognitive bias in young adult males.

Conclusions

Overall, the association between negative cognitive bias, hippocampal neurogenesis, and inflammation in the brain differs by age and sex. Hippocampal neurogenesis and inflammation may play greater role in the cognitive bias of young males compared to a greater role of BLA inflammation in adult females. These findings lay the groundwork for the discovery of sex-specific novel therapeutics that target region-specific inflammation in the brain and hippocampal neurogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12979-022-00299-4.

• Adolescent male rats had more hippocampal inflammation than females after cognitive bias testing.

• Adult female rats had more basolateral amygdalar inflammation than males after cognitive bias testing.

• HPC neurogenesis was negatively associated to cognitive bias in young adult male rats.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

Evaluating the role of perceived injustice in mental health outcomes in cervical cancer care

Purpose

Perceived injustice is a novel psychosocial construct which reflects negative cognitive appraisals of blame, unfairness, and the severity and irreparability of one’s loss. Experiences of injustice are increasingly recognised as a key determinant of recovery outcomes in healthcare. The aim of this study was to explore the impact of perceived injustice on psychological outcomes amongst a group of cancer patients and survivors who received false-negative smear results under a National Cervical Cancer Screening Programme (CervicalCheck).

Methods

Women who received false-negative smear results who were involved in the CervicalCheck controversy in Ireland completed online measures of perceived injustice (IEQ), psychological distress (depression and anxiety as measured by the HADS), and satisfaction with care (PSCC) (n = 144).

Results

Rates of psychological distress in this sample were high, with 76% scoring in the clinical range for anxiety, 51% in the clinical range for depression, and 88% in the clinical range for perceived injustice. Hierarchical regression analyses revealed that perceived injustice contributed unique variance to the prediction of depression and anxiety. Satisfaction with care significantly moderated the association between perceived injustice and depression.

Conclusions

Cancer patients who report high levels of perceived injustice are at greater risk for experiencing psychological distress. The relationship between perceived injustice and depression may vary as a function of satisfaction with care. Addressing issues of perceived injustice in the psychosocial and rehabilitative care of cancer patients may support the early identification of those at risk of significant psychological distress and enhance intervention success. Implications for policy and practice in healthcare are discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00520-022-07060-z.

Examining the associations between PTSD symptoms and aspects of emotion dysregulation through network analysis

Despite the clearly established link between posttraumatic stress disorder (PTSD) and emotion dysregulation, little is known about how individual symptoms of PTSD and aspects of emotion dysregulation interrelate. The network approach to mental health disorders provides a novel framework for conceptualizing the association between PTSD and emotion dysregulation as a system of interacting nodes. In this study, we estimated the structural relations among PTSD symptoms and aspects of emotion dysregulation within a large sample of women who participated in a multi-site study of sexual revictimization (N = 463). We estimated expected influence to reveal differential associations among PTSD symptoms and aspects of emotion dysregulation. Further, we estimated bridge expected influence to identify influential nodes connecting PTSD symptoms and aspects of emotion dysregulation. Results highlighted the key role of concentration difficulties in expected influence and bridge expected influence. Findings highlight several PTSD symptoms and aspects of emotion dysregulation that may be targets for future intervention.

Towards better hypothesis tests in oxytocin research: Evaluating the validity of auxiliary assumptions

Various factors have been attributed to the inconsistent reproducibility of human oxytocin research in the cognitive and behavioral sciences. These factors include small sample sizes, a lack of pre-registered studies, and the absence of overarching theoretical frameworks that can account for oxytocin's effects over a broad range of contexts. While there have been efforts to remedy these issues, there has been very little systematic scrutiny of the role of auxiliary assumptions, which are claims that are not central for testing a hypothesis but nonetheless critical for testing theories. For instance, the hypothesis that oxytocin increases the salience of social cues is predicated on the assumption that intranasally administered oxytocin increases oxytocin levels in the brain. Without robust auxiliary assumptions, it is unclear whether a hypothesis testing failure is due to an incorrect hypothesis or poorly supported auxiliary assumptions. Consequently, poorly supported auxiliary assumptions can be blamed for hypothesis failure, thereby safeguarding theories from falsification. In this article, I will evaluate the body of evidence for key auxiliary assumptions in human behavioral oxytocin research in terms of theory, experimental design, and statistical inference, and highlight assumptions that require stronger evidence. Strong auxiliary assumptions will leave hypotheses vulnerable for falsification, which will improve hypothesis testing and consequently advance our understanding of oxytocin's role in cognition and behavior.

Bipolar disorder and regretted behavior in relation to use of social media and online dating

OBJECTIVES

Individuals with bipolar disorder are prone to risk-taking behavior that is subsequently regretted. Here, we investigated whether this also occurs in relation to the use of social media and online dating.

METHODS

A questionnaire-based survey focusing on the use of social media and online dating was conducted among individuals attending an outpatient clinic for bipolar disorder, and among individuals attending two general practices in the same region (controls). The association between bipolar disorder and self-reported regretted behavior on social media/online dating sites was investigated using logistic regression with adjustment for age and sex.

RESULTS

A total of 124 individuals with bipolar disorder and 196 individuals without affective disorder from the general practices (controls) formed the study sample. Among the individuals with bipolar disorder who used social media, 66% reported regretted behavior as a consequence of this use, whereas only 31% of the controls reported such behavior. The corresponding numbers for individuals who used online dating were 65% for those with bipolar disorder and 31% for the controls. Following adjustment for age and sex, bipolar disorder was associated with elevated risk of regretted behavior in relation to the use of both social media (adjusted odds ratio: 3.6, 95% CI: 2.2, 5.9) and online dating (adjusted odds ratio: 4.1, 95% CI: 2.1, 8.0).

CONCLUSIONS

These findings suggest that risk-taking behavior and subsequent regret among individuals with bipolar disorder extend to social media and online dating. Cautious use of these platforms may be particularly relevant for individuals with bipolar disorder.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.

Revision and psychometric properties of the negative cognitive processing bias scale

Negative cognitive processing bias (NCPB) is a cognitive trait that makes individuals more inclined to prioritize negative external stimuli (cues) when processing information. Cognitive biases have long been observed in mood and anxiety disorders, improving validation of tools to measure this phenomenon will aid us to determine whether there is a robust relationship between NCPB and major depressive disorder, anxiety disorders and other clinical disorders. Despite the development of an initial measure of this trait, that is, the negative cognitive processing bias questionnaire (NCPBQ), the lack of psychometric examinations and applications in large-scale samples hinders the determination of its reliability and validity and further limits our understanding of how to measure the NCPB traits of individuals accurately. To address these issues, the current study evaluated the psychometric properties of the NCPBQ in a large-scale sample (n = 6,069), which was divided into two subsamples (Subsample 1, n = 3,035, serving as the exploratory subsample, and Subsample 2, n = 3,034, serving as the validation subsample), and further revised it into a standardized scale, that is the negative cognitive processing bias scale (NCPBS), based on psychometric constructs. The results show that NCPBS possesses good construct reliability, internally consistent reliability, and test-retest reliability. Furthermore, by removing two original items from NCPBQ, NCPBS was found to have good criterion-related validity. In conclusion, the present study provides a reliable and valid scale for assessing negative cognitive processing bias of individuals.

Self-referential Processing in Remitted Depression: An Event-Related Potential Study

Background

Identifying 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.

Methods

At 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.

Results

Drift 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.

Conclusions

Self-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.

Social media use, sleep, and psychopathology in psychiatrically hospitalized adolescents

Sleep disruption among adolescents represents a major public health concern, and social media use may play an important role in affecting sleep and subsequent mental health. While prior studies of youth sleep and mental health have often focused on social media use frequency and duration, adolescents' emotional experiences related to social media have been underexplored, particularly among clinically acute populations. This study offers a preliminary investigation of associations among negative emotional experiences using social media, sleep disturbance, and clinical symptom severity in a sample of psychiatrically hospitalized youth. A sample of 243 adolescents (Mage = 15.34) completed self-report measures at a single time point. Measures assessed social media use, including frequency and duration, subjective experiences of use, and emotional responses to use, as well as sleep disturbance and clinical symptom severity, including suicidal ideation, internalizing symptoms, and attention problems. Results revealed that more frequent negative emotional responses to social media use were linked to greater sleep disturbance and higher clinical symptom severity. Furthermore, sleep disturbance mediated the relation between negative emotional responses to social media and clinical symptom severity. While gender differences were revealed in characteristics of social media use, sleep disturbance, and clinical outcomes, the associations among these constructs did not vary across gender groups. Overall, these findings highlight sleep disturbance as a potential mechanism through which negative emotional experiences on social media may impact clinical symptoms in psychiatrically vulnerable youth.

Subjective cognitive assessments and N-back are not correlated, and they are differentially affected by anxiety and depression

Cognitive function (CF) is a core feature related to all psychiatric disorders. However, self-report scales of CF (SRSC) may not always correlate with CF's objective measures and may have different mediators. Tools to select for evaluating CF in diverse psychiatric populations and their determinants need to be studied. In this study, we aimed to assess the association of SRSC (Perceived Deficit Questionnaire-Depression (PDQ-D), and World Health Organization's Adult Attention Deficit Hyperactivity Disorder Self-Report Scale (ASRS) and its inattentiveness subscale) with Letter-N-back as an objective measure of CF, and to analyze their association with psychopathology. Two hundred nine (131 nonclinical, and 78 clinical with a psychiatric diagnosis of ICD10 F31-39 [mood disorders excluding Bipolar I] or F40-F49 [neurotic, stress-related or psychosomatic disorder] categories) participants were evaluated with PDQ-D, ASRS, Beck Depression Inventory (BDI), and Beck's Anxiety Inventory (BAI), and N-back. Both groups' data were included in the analysis. PDQ-D showed a small correlation with N-back scores, whereas ASRS showed no correlation. PDQ-D and ASRS showed a large correlation. Age and BAI scores significantly predicted both PDQ-D and ASRS, whereas the cognitive subscale of BDI predicted PDQ-D, but not ASRS. Only BAI scores predicted N-back results. The mediation model revealed that 2-back scores of N-back task directly affects PDQ-D scores, independent of BDI scores. However, the cognitive subscale of BDI moderated 2-back and PDQ-D association. On the contrary, BAI scores significantly mediated the association of 2-back scores with PDQ-D. The direct effect of 2-back scores in PDQ-D was insignificant in the mediation of BAI scores. Our study validates the discordance between SRSC and an objective measurement of CF. Anxiety may affect both self-report and objective measurement of CF, whereas depressive thought content may lead to higher cognitive dysfunction reports in nondemented participants.

The effect of self-referential processing on anxiety in response to naturalistic and laboratory stressors

Although stressful life events increase risk for symptoms of Generalised Anxiety Disorder (GAD), we know little about mechanisms that increase GAD symptoms during times of stress. Despite evidence that self-referential processing contributes to other forms of psychopathology, namely depression, it is unknown whether self-referential processing also contributes to symptoms of GAD. Thus, we examined the association of self-referential processing with GAD symptoms in response to a naturalistic stressor (Study 1; n=135) and with anxiety-tension in response to a laboratory stressor (Study 2; n=56). In Study 1, participants completed the self-referential encoding task (SRET) in their initial weeks of university, and we assessed GAD symptoms four times across the semester. In Study 2, participants completed the SRET immediately before a laboratory stressor, and we assessed moment-to-moment changes in anxiety-tension. Greater negatively biased self-referential processing was associated with higher GAD symptoms at the start of university and greater reactivity to the laboratory stressor. In contrast, greater positively biased self-referential processing served as a protective factor associated with greater decline in symptoms over time. This study is the first to demonstrate that there are valence-specific effects of self-referential processing on anxiety, suggesting that self-referential processing may be relevant to GAD.

Predicting Mental Health Treatment Access Among Adolescents With Elevated Depressive Symptoms: Machine Learning Approaches

A large proportion of adolescents experiencing depression never access treatment. To increase access to effective mental health care, it is critical to understand factors associated with increased versus decreased odds of adolescent treatment access. This study used individual depression symptoms and sociodemographic variables to predict whether and where adolescents with depression accessed mental health treatments. We performed a pre-registered, secondary analysis of data from the 2017 National Survey of Drug Use and Health (NSDUH), a nationally representative sample of non-institutionalized civilians in the United States. Using four cross-validated random forest models, we predicted whether adolescents with elevated past-year depressive symptoms (N = 1,671; ages 12-17 years) accessed specific mental health treatments in the previous 12 months ("yes/no" for inpatient, outpatient, school, any). 53.38% of adolescents with elevated depressive symptoms accessed treatment of any kind. Even with depressive symptoms and sociodemographic factors included as predictors, pre-registered random forests explained < 0.00% of pseudo out-of-sample deviance in adolescent access to inpatient, outpatient, school, or overall treatments. Exploratory elastic net models explained 0.80-2.50% of pseudo out-of-sample deviance in adolescent treatment access across all four treatment types. Neither individual depressive symptoms nor any socioeconomic variables meaningfully predicted specific or overall mental health treatment access in adolescents with elevated past-year symptoms. This study highlights substantial limitations in our capacity to predict whether and where adolescents access mental health treatment and underscores the broader need for more accessible, scalable adolescent depression treatments.

Neurophysiological Markers Related to Negative Self-referential Processing Differentiate Adolescent Suicide Ideators and Attempters

Background

Adolescent suicide is a major public health concern, and presently, there is a limited understanding of the neurophysiological correlates of suicidal behaviors. Cognitive models of suicide indicate that negative views of the self are related to suicidal thoughts and behaviors, and this study investigated whether behavioral and neural correlates of self-referential processing differentiate suicide ideators from recent attempters.

Methods

Adolescents with depression reporting current suicidal ideation and no lifetime suicide attempts (suicide ideators, n = 30) and past-year suicide attempts (recent attempters, n = 26) completed a self-referential encoding task while high-density electroencephalogram data were recorded. Behavioral analyses focused on negative processing bias (i.e., tendency to attribute negative information as being self-relevant) and drift rate (i.e., slope of reaction time and response type that corresponds to how quickly information is accumulated to make a decision about whether words are self-referent). Neurophysiological markers probing components reflecting early semantic monitoring (P2), engagement (early late positive potential), and effortful encoding (late late positive potential) also were tested.

Results

Adolescent suicide ideators and recent suicide attempters reported comparable symptom severity, suicide ideation, and mental disorders. Although there were no behavioral differences, compared with suicide ideators, suicide attempters exhibited greater P2 amplitudes for negative versus positive words, which may reflect enhanced attention and arousal in response to negative self-referential stimuli. There were no group differences for the early or late late positive potential.

Conclusions

Enhanced sensory arousal in response to negative stimuli-that is, attentional orienting to semantic, emotional, and self-relevant features-differentiates adolescent suicide attempters from ideators and thus may signal risk for suicidal behavior.

Traditional Chinese Medicine Body Constitutions and Psychological Determinants of Depression among University Students in Malaysia: A Pilot Study

Depression is commonly observed in university students, who are a high risk group for developing psychiatric disorders during adulthood. This study aimed to determine the prevalence of depression and its traditional Chinese medicine body constitutions and psychological determinants among university students in Malaysia. A cross-sectional pilot study was conducted between 9 and 28 September 2020 among 80 university students in Malaysia. Participants completed online survey questionnaires, including the validated Patient Health Questionnaire (PHQ-9), Constitution in Chinese Medicine Questionnaire (CMCQ), Dysfunctional Attitude Scale (DAS), Depression Anxiety Stress Scale (DASS-21) stress subscale, Perceived Stress Scale (PSS-10), and Rosenberg Self-Esteem Scale (RSES), which assess depression, body constitution, dysfunctional attitude, stress, perceived stress, and self-esteem. Multiple linear regression analyses were performed to determine the associated risk factors for depression. The overall prevalence of depression among university students was 33.8%. The multiple regression analysis showed a significant relationship between depression and qi-stagnation constitution (B = 0.089, p = 0.011), balanced constitution (B = -0.077, p = 0.049), and self-esteem (B = -0.325, p = 0.001). Our findings suggest that some traditional Chinese medicine body constitutions and self-esteem are significant risk factors affecting depression among university students. Identifying risk factors of depression is vital to aid in the early detection of depression among university students.