scholarly journals Anhedonia sub-components and reward and effort learning for primary and secondary rewards in young people with depression symptoms

2021 ◽  
Author(s):  
Anna-Lena Frey ◽  
Siyabend Kaya ◽  
Irina Adeniyi ◽  
Ciara McCabe
Keyword(s):  
Depressive Symptoms ◽  
Young People ◽  
Learning Task ◽  
Reward Processing ◽  
Reward Learning ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Treatment Development ◽  
Anticipatory Anhedonia ◽  
Completion Times

Background: Anhedonia, a central depression symptom, is associated with impairments in reward processing. However how the sub-components of reward processing (anticipation, motivation, consummation and learning) are related to depression symptoms is not well understood. In particular, little is known about how effort cost and reward learning is related to anhedonia.Methods: We recruited young people with high (N=50) and low (N=88) depression symptoms and assessed their learning, consummatory, anticipatory, and motivational responses within an effort and reward learning task. To increase the reward attractiveness, especially for younger people, we included not only money (secondary reward), but also chocolate tastes and puppy images (primary rewards).Results: Across all participants, we found that self-reported willingness to exert effort positively correlated with actual effort exertion and negatively with effort completion times. We also observed higher accuracy for reward learning vs. effort learning. Additionally, effort expenditure differed between reward types, although no differences in reward liking were observed. Interestingly, we also found that higher anticipatory anhedonia was associated with lower reward learning accuracy. Limitations: The study assessed only depressive symptoms, not clinically diagnosed major depression. Conclusion: To our knowledge, this is the first study to examine reward and effort learning simultaneously in young people with depression symptoms. Our findings suggest a differentiation between motivational and consummatory responses, as well as between reward and effort learning. Moreover, we show that anticipatory anhedonia is related to reward learning. Understanding the link between objective reward processing and anhedonia sub-types could provide new targets for treatment development.

Bereavement or Breakup: A Network Approach to Modeling Differential Pathways into Depression

2019 ◽  
Author(s):  
Julian Burger ◽  
Margaret S. Stroebe ◽  
Pasqualina Perrig-Chiello ◽  
Henk A.W. Schut ◽  
Stefanie Spahni ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Graphical Models ◽  
Contextual Factors ◽  
Marital Disruption ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Multiple Time ◽  
Cross Sectional ◽  
Network Analyses ◽  
Within Subjects

Background: Prior network analyses demonstrated that the death of a loved one potentially precedes specific depression symptoms, primarily loneliness, which in turn links to other depressive symptoms. In this study, we extend prior research by comparing depression symptom network structures following two types of marital disruption: bereavement versus separation. Methods: We fitted two Gaussian Graphical Models to cross-sectional data from a Swiss survey of older persons (145 bereaved, 217 separated, and 362 married controls), and compared symptom levels across bereaved and separated individuals. Results: Separated compared to widowed individuals were more likely to perceive an unfriendly environment and oneself as a failure. Both types of marital disruption were linked primarily to loneliness, from where different relations emerged to other depressive symptoms. Amongst others, loneliness had a stronger connection to perceiving oneself as a failure in separated compared to widowed individuals. Conversely, loneliness had a stronger connection to getting going in widowed individuals. Limitations: Analyses are based on cross-sectional between-subjects data, and conclusions regarding dynamic processes on the within-subjects level remain putative. Further, some of the estimated parameters in the network exhibited overlapping confidence intervals and their order needs to be interpreted with care. Replications should thus aim for studies with multiple time points and larger samples. Conclusions: The findings of this study add to a growing body of literature indicating that depressive symptom patterns depend on contextual factors. If replicated on the within-subjects level, such findings have implications for setting up patient-tailored treatment approaches in dependence of contextual factors.

A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People

2021 ◽  
pp. 105984052110126
Author(s):  
Jia-Wen Guo ◽  
Brooks R. Keeshin ◽  
Mike Conway ◽  
Wendy W. Chapman ◽  
Katherine A. Sward
Keyword(s):  
Content Analysis ◽  
Depressive Symptoms ◽  
Young People ◽  
Health Care Providers ◽  
Scoping Review ◽  
School Nurses ◽  
Future Research ◽  
Depression Symptom ◽  
Care Providers ◽  
Five Dimensions

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral ( n = 8), cognitive ( n = 14), emotional ( n = 15), interpersonal ( n = 13), and somatic ( n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.

scholarly journals FKBP5 Genotype Linked to Combined PTSD-Depression Symptom in Chinese Earthquake Survivors

2019 ◽  
Vol 64 (12) ◽  
pp. 863-871
Author(s):  
Gen Li ◽  
Li Wang ◽  
Kunlin Zhang ◽  
Chengqi Cao ◽  
Xing Cao ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Genetic Basis ◽  
Latent Profile Analysis ◽  
Common Mental Disorders ◽  
Phenotypic Heterogeneity ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Environment Interaction ◽  
Post Traumatic Stress ◽  
2008 Wenchuan Earthquake

Background: Post-traumatic stress disorder (PTSD) and depression are common mental disorders in individuals experiencing traumatic events. To date, few studies have studied the relationship between genetic basis and phenotypic heterogeneity of traumatized individuals. The present study examined the effects of four FKBP5 SNPs (rs1360780, rs3800373, rs9296158, and rs9470080) in four postdisaster groups (low symptom, predominantly depressive, predominantly PTSD, and combined PTSD-depression symptom groups) as identified by latent profile analysis. Methods: A total of 1,140 adults who experienced the 2008 Wenchuan earthquake participated in our study. Earthquake-related trauma, PTSD, and depressive symptoms were measured using standard psychometric instruments. The four FKBP5 SNPs were genotyped using a custom-by-design 2 × 48-Plex SNP scan™ Kit. Results: After adjusting for covariates, the main and gene–environment interaction effects of rs9470080 were all significant when the combined PTSD-depression group was compared with the low symptoms, predominantly depression and predominantly PTSD groups. rs9470080 TT genotype carriers had a higher risk of developing high co-occurring PTSD and depression symptoms than the C allele carriers. However, when trauma exposure was severe, the TT genotype carriers and C allele carriers did not differ in the risk of developing high co-occurring PTSD and depressive symptoms. The other three SNPs demonstrated no significant effects. Moreover, the rs3800373-rs9296158-rs1360780-rs9470080 haplotype A-G-C-T was found significantly associated with combined PTSD-depression symptoms. Conclusion: Our findings support the genetic basis of phenotypic heterogeneity in people exposed to trauma. Furthermore, the results reveal the possibility that the variants of FKBP5 gene may be associated with depression-PTSD comorbidity.

scholarly journals THE RELATIONSHIP BETWEEN MARITAL STATUS, COHORT, AND DEPRESSION SYMPTOMS IN TAIWAN

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S311-S312
Author(s):  
Fang-Yi Huang ◽  
Min Li
Keyword(s):  
Depressive Symptoms ◽  
Marital Status ◽  
Baby Boom ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Economic Decline ◽  
Logistic Regression Models ◽  
Financial Decline ◽  
Married Female ◽  
The Relationship

Abstract Objectives: The relationship between marital status and depression symptoms is well documented. However, how the negative economic shock affect relationship differ by gender and cohort is still indecisive. The dataset “2011 wave of the Taiwan Longitudinal Study in Aging” and logistic regression models were used in the study. The results: Marital status is related to depression symptoms, but it differs by gendered cohort. With considering financial shock, there is no difference of depressive symptom between divorced and married female. The divorced and widowed have 4.81 and 2.47 times higher of getting depression symptom than the married for baby boom female. Being divorced is 3.67 times higher of getting depressive symptoms than being married for baby boom male. For WWII female, the widows are 1.78 times higher to have depressive symptoms than the married. being divorced, widowers, and single are 3.32, 2.21 and 2.90 times higher of getting depressive symptoms than being married for WWII male. Being divorced is 3.67 times higher of getting depressive symptoms than being married for baby boom male. In conclusions, people with unstable marital statuses are more depressed than the married. In particular, the effect of unstable marital statuses on depression could be account for by financial decline for women but not men. Given the policy emphasis on those with unstable marital status and economic decline, divorce female and single baby boom female may represent particular groups in whom interventions designed to financially support.

scholarly journals Deep learning for prediction of depressive symptoms in a large textual dataset

2021 ◽  
Author(s):  
Md Zia Uddin ◽  
Kim Kristoffer Dysthe ◽  
Asbjørn Følstad ◽  
Petter Bae Brandtzaeg
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Depressive Symptoms ◽  
Intelligent Systems ◽  
Superior Performance ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Information Channel ◽  
Symptoms Of Depression ◽  
Prediction Approach

AbstractDepression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

scholarly journals Comparison of conventional and rapid-acting antidepressants in a rodent probabilistic reversal learning task

2020 ◽  
Vol 4 ◽  
pp. 239821282090717 ◽  
Author(s):  
Matthew P. Wilkinson ◽  
John P. Grogan ◽  
Jack R. Mellor ◽  
Emma S. J. Robinson
Keyword(s):  
Reinforcement Learning ◽  
Reversal Learning ◽  
Antidepressant Treatment ◽  
Learning Task ◽  
Reward Processing ◽  
Reward Learning ◽  
Feedback Sensitivity ◽  
Rule Changes ◽  
Reinforcement Learning Model ◽  
Probabilistic Reversal Learning

Deficits in reward processing are a central feature of major depressive disorder with patients exhibiting decreased reward learning and altered feedback sensitivity in probabilistic reversal learning tasks. Methods to quantify probabilistic learning in both rodents and humans have been developed, providing translational paradigms for depression research. We have utilised a probabilistic reversal learning task to investigate potential differences between conventional and rapid-acting antidepressants on reward learning and feedback sensitivity. We trained 12 rats in a touchscreen probabilistic reversal learning task before investigating the effect of acute administration of citalopram, venlafaxine, reboxetine, ketamine or scopolamine. Data were also analysed using a Q-learning reinforcement learning model to understand the effects of antidepressant treatment on underlying reward processing parameters. Citalopram administration decreased trials taken to learn the first rule and increased win-stay probability. Reboxetine decreased win-stay behaviour while also decreasing the number of rule changes animals performed in a session. Venlafaxine had no effect. Ketamine and scopolamine both decreased win-stay probability, number of rule changes performed and motivation in the task. Insights from the reinforcement learning model suggested that reboxetine led animals to choose a less optimal strategy, while ketamine decreased the model-free learning rate. These results suggest that reward learning and feedback sensitivity are not differentially modulated by conventional and rapid-acting antidepressant treatment in the probabilistic reversal learning task.

scholarly journals Stressful life events moderate the effect of neural reward responsiveness in childhood on depressive symptoms in adolescence

2019 ◽  
Vol 50 (9) ◽  
pp. 1548-1555 ◽  
Author(s):  
Brandon L. Goldstein ◽  
Ellen M. Kessel ◽  
Autumn Kujawa ◽  
Megan C. Finsaas ◽  
Joanne Davila ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Life Events ◽  
Stressful Life Events ◽  
Community Sample ◽  
Reward Processing ◽  
Event Related Potential ◽  
Self Report ◽  
Stressful Life ◽  
Depression Symptoms

AbstractBackgroundReward processing deficits have been implicated in the etiology of depression. A blunted reward positivity (RewP), an event-related potential elicited by feedback to monetary gain relative to loss, predicts new onsets and increases in depression symptoms. Etiological models of depression also highlight stressful life events. However, no studies have examined whether stressful life events moderate the effect of the RewP on subsequent depression symptoms. We examined this question during the key developmental transition from childhood to adolescence.MethodsA community sample of 369 children (mean age of 9) completed a self-report measure of depression symptoms. The RewP to winning v. losing was elicited using a monetary reward task. Three years later, we assessed stressful life events occurring in the year prior to the follow-up. Youth depressive symptoms were rated by the children and their parents at baseline and follow-up.ResultsStressful life events moderated the effect of the RewP on depression symptoms at follow-up such that a blunted RewP predicted higher depression symptoms in individuals with higher levels of stressful life events. This effect was also evident when events that were independent of the youth's behavior were examined separately.ConclusionsThese results suggest that the RewP reflects a vulnerability for depression that is activated by stress.

scholarly journals What causes depression in adults?

2015 ◽  
Vol 125 (2) ◽  
pp. 116-120 ◽  
Author(s):  
Marta Bembnowska ◽  
Jadwiga Jośko-Ochojska
Keyword(s):  
Depressive Symptoms ◽  
Young People ◽  
Risky Behaviors ◽  
Young Person ◽  
Depression Symptoms ◽  
Breeding Ground ◽  
Symptoms Of Depression ◽  
The Family ◽  
Long Time ◽  
Cultural Components

Abstract The problem of depression in adolescents is discussed increasingly more often. A lot of researchers devote their careers to investigating this subject. The issue becomes vital, since the number of young people with depressive symptoms is constantly on the rise. The diagnosis can be difficult, as many a time the changes so typical for the puberty period appear. They include mood swings, explosiveness, propulsion disorders, puissance, insomnia, concentration problems etc. These might be the first symptoms of depression as well. It is impossible to point to one cause of depression because it is a disease conditioned by many different factors, ranging from independent factors like genetic, biological, hormonal, through the influence of the family or the environment influence and socio-cultural components. Early depression symptoms, long time exposure to stress, challenges or adversities - things every young person has to deal with - are a breeding ground for risky behaviors among adolescents. Teens are more likely to reach for different kinds of stimulants like alcohol, cigarettes or drugs etc. It has also been proven that anti-health behaviors may cause depression in the future

scholarly journals Extraversion but not Depression Predicts Implicit Reward Sensitivity: Revisiting the Measurement of Anhedonic Phenotypes

2019 ◽  
Author(s):  
Scott D. Blain ◽  
Tyler A. Sassenberg ◽  
Muchen Xi ◽  
Daiqing Zhao ◽  
Colin G. DeYoung
Keyword(s):  
Depressive Symptoms ◽  
Response Bias ◽  
Big Five ◽  
Structural Equation ◽  
Differential Reinforcement ◽  
Structural Equation Models ◽  
Community Sample ◽  
Learning Task ◽  
Reward Sensitivity ◽  
Reward Learning

Recently, increasing efforts have been made to define and measure dimensional phenotypes associated with psychiatric disorders. One example is an implicit reward learning task developed by Pizzagalli et al. (2005) to assess anhedonia, by measuring participants’ responses to a differential reinforcement schedule. This task has been used in many studies, which have connected blunted reward response in the task to depressive symptoms, across clinical groups and in the general population. The current study attempted to replicate these findings in a large community sample and also investigated possible associations with Extraversion, a personality trait linked theoretically and empirically to reward sensitivity. Participants (N = 299) completed the reward-learning task, as well as the Beck Depression Inventory, Personality Inventory for the DSM-5, Big Five Inventory, and Big Five Aspect Scales. Our direct replication attempts used bivariate analyses of observed variables and ANOVA models. Follow-up and extension analyses used structural equation models to assess relations among latent reward sensitivity, depression, Extraversion, and Neuroticism. No significant associations were found between reward sensitivity (i.e., response bias) and depression, thus failing to replicate previous findings. Response bias and change in response bias showed significant positive associations with Extraversion, but not with Neuroticism. Findings suggest reward sensitivity as measured by this implicit reward learning task may be related primarily to Extraversion and its pathological manifestations, rather than to depression per se, consistent with existing models that conceptualize depressive symptoms as combining features of Neuroticism and low Extraversion.

Depression symptoms in late life assessed using the EURO–D scale

1999 ◽  
Vol 174 (4) ◽  
pp. 339-345 ◽  
Author(s):  
M. J. Prince ◽  
A. T. F. Beekman ◽  
D. J. H. Deeg ◽  
R. Fuhrer ◽  
S.-L. Kivela ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Gender Difference ◽  
Cognitive Decline ◽  
Marital Status ◽  
Older Persons ◽  
Positive Association ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Symptom Scale ◽  
Two Factors

BackgroundData from surveys involving 21 724 subjects aged ⩾65 years were analysed using a harmonised depression symptom scale, the EURO–D.AimsTo describe and compare the effects of age, gender and mental status on depressive symptoms across Europe.MethodWe tested for the effects of centre, age, gender and marital status on EURO–D score. Between-centre variance was partitioned according to centre characteristics: region, religion and survey instrument used.ResultsEURO–D scores tended to increase with age, women scored higher than men, and widowed and separated subjects scored higher than others. The EURO–D scale could be reduced into two factors: affective suffering, responsible for the gender difference, and motivation, accounting for the positive association with age.ConclusionsLarge between-centre differences in depression symptoms were not explained by demography or by the depression measure used in the survey. Consistent, small effects of age, gender and marital status were observed across Europe. Depression may be overdiagnosed in older persons because of an increase in lack of motivation that may be affectively neutral, and is possibly related to cognitive decline.

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