scholarly journals Associations between aversive learning processes and transdiagnostic psychiatric symptoms revealed by large-scale phenotyping

2019 ◽  
Author(s):  
Toby Wise ◽  
Raymond J Dolan
Keyword(s):  
Large Scale ◽  
Psychiatric Symptoms ◽  
Learning Task ◽  
Uncertainty Estimation ◽  
Learning Processes ◽  
Self Report ◽  
Related Factor ◽  
Least Squares Regression ◽  
Aversive Learning ◽  
Uncertain Environments

AbstractBackgroundSymptom expression in a range of psychiatric conditions is linked to altered threat perception, manifesting particularly in uncertain environments. How precise computational mechanisms that support aversive learning, and uncertainty estimation, relate to the presence of specific psychiatric symptoms remains undetermined. 400 subjects completed an online game-based aversive learning task, requiring avoidance of negative outcomes, in conjunction with completing measures of common psychiatric symptoms. We used a probabilistic computational model to measure distinct processes involved in learning, in addition to inferred estimates of safety likelihood and uncertainty, and tested for associations between these variables and traditional psychiatric constructs alongside transdiagnostic dimensions. We used partial least squares regression to identify components of psychopathology grounded in both aversive learning behaviour and symptom self-report. We show that state anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced learning from safety, and data-driven analysis indicated the presence of two separable components across our behavioural and questionnaire data: one linked enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linked enhanced threat learning, and heightened uncertainty estimation, to symptoms of depression and social anxiety. Our findings implicate distinct aversive learning processes in the expression of psychiatric symptoms that transcend traditional diagnostic boundaries.

scholarly journals Beneficial Effects of Opioid Rotation to Buprenorphine/Naloxone on Opioid Misuse, Craving, Mental Health, and Pain Control in Chronic Non-Cancer Pain Patients with Opioid Use Disorder

2021 ◽  
Vol 10 (16) ◽  
pp. 3727
Author(s):  
Arnt F. A. Schellekens ◽  
Stijn E. Veldman ◽  
Eka S. D. Suranto ◽  
Steffie M. van Rijswijk ◽  
Selina E. I. van der Wal ◽  
...  
Keyword(s):  
Cancer Pain ◽  
Large Scale ◽  
Psychiatric Symptoms ◽  
Opioid Use Disorder ◽  
Self Report ◽  
Inpatient Setting ◽  
Opioid Misuse ◽  
Opioid Use ◽  
Opioid Agonist ◽  
The Mean

Patients with chronic non-cancer pain (CNCP) often use opioids for long periods of time. This may lead to opioid use disorder (OUD) and psychiatric symptoms: mainly depression and anxiety. The current study investigated the effect of buprenorphine/naloxone (BuNa) rotation on opioid misuse, craving, psychiatric symptoms and pain in patients with CNCP and OUD. Forty-three participants with CNCP and OUD were converted from a full mu-receptor agonist opioid (mean morphine equivalent dose: 328.3 mg) to BuNa, in an inpatient setting. Opioid misuse, craving, co-occurring psychiatric symptoms, and pain perception were determined at baseline and after a two-month follow-up, using the following self-report questionnaires: Current Opioid Misuse Measurement (COMM), Visual Analog Scale (VAS-craving and VAS-pain) and Depression, Anxiety and Stress Scale (DASS), respectively. VAS-craving and VAS-pain were also determined immediately after conversion. A total of 37 participants completed the protocol. The mean COMM decreased from 17.1 to 6.7 (F = 36.5; p < 0.000), the mean VAS-craving decreased from 39.3 to 5.3 (−86.6%; F = 26.5, p < 0.000), the mean DASS decreased from 12.1 to 6.6 (F = 56.3, p < 0.000), and the mean VAS-pain decreased from 51.3 to 37.2 (−27.4%, F = 3.3; p = 0.043). Rotation to BuNa in patients with CNCP and OUD was accompanied by reductions in (i) opioid misuse, (ii) opioid craving, (iii) the severity of co-occurring psychiatric symptoms, and (iv) self-reported pain. BuNa as opioid agonist treatment may therefore be a beneficial strategy in CNCP patients with OUD. The limited sample size and the observational nature of this study underline the need for the replication of the current findings in large-scale, controlled studies.

Crisis Occurrence and Resolution in Patients with Severe and Persistent Mental Illness

Crisis ◽  
2005 ◽  
Vol 26 (4) ◽  
pp. 160-169 ◽  
Author(s):  
Paul S. Links ◽  
Rahel Eynan ◽  
Jeffrey S. Ball ◽  
Aiala Barr ◽  
Sean Rourke
Keyword(s):  
Mental Illness ◽  
Suicide Ideation ◽  
Rating Scale ◽  
Psychiatric Symptoms ◽  
Self Report ◽  
Community Treatment ◽  
Persistent Mental Illness ◽  
Crisis Events ◽  
Symptom Distress Scale ◽  
Distress Scale

Abstract. Assertive community treatment appears to have limited impact on the risk of suicide in persons with severe and persistent mental illness (SPMI). This exploratory prospective study attempts to understand this observation by studying the contribution of suicidality to the occurrence of crisis events in patients with SPMI. Specifically, an observer-rated measure of the need for hospitalization, the Crisis Triage Rating Scale, was completed at baseline, crisis occurrence, and resolution to determine how much the level of suicidality contributed to the deemed level of crisis. Second, observer-ratings of suicidal ideation, the Modified Scale for Suicide Ideation, and psychopathology and suicidality, Brief Psychiatric Rating Scale, were measured at baseline, crisis occurrence, and resolution. A self-report measure of distress, the Symptom Distress Scale, was completed at baseline, crisis occurrence, and resolution. Finally, the patients' crisis experiences were recorded qualitatively to compare with quantitative measures of suicidality. Almost 40% of the subjects experienced crisis events and more than a quarter of these events were judged to be severe enough to warrant the need for hospitalization. Our findings suggest that elevation of psychiatric symptoms is a major contributor to the crisis occurrences of individuals with SPMI; although the risk of suicide may have to be conceived as somewhat separate from crisis occurrence.

The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review

2020 ◽  
Vol 20 (7) ◽  
pp. 540-553 ◽  
Author(s):  
Anna Todeva-Radneva ◽  
Rositsa Paunova ◽  
Sevdalina Kandilarova ◽  
Drozdstoy St. Stoyanov
Keyword(s):  
Psychiatric Disorders ◽  
Psychiatric Symptoms ◽  
Medical Science ◽  
Self Report ◽  
Current Status ◽  
Diagnostic Tools ◽  
Main Concept ◽  
Directional Change ◽  
The Mind ◽  
Neuroimaging Techniques

: Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being “brain disorders”. It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. : The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. : In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.

scholarly journals Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Aurélien Weiss ◽  
Valérian Chambon ◽  
Junseok K. Lee ◽  
Jan Drugowitsch ◽  
Valentin Wyart
Keyword(s):  
Cognitive Processes ◽  
Learning Task ◽  
Human Learning ◽  
Sensory Cues ◽  
Uncertain Environments ◽  
Behavioral Difference ◽  
Neural Interaction ◽  
Degree Of Control ◽  
Uncertain Outcomes ◽  
State Inference

AbstractMaking accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

scholarly journals Obsessive–compulsive symptoms and information seeking during the Covid-19 pandemic

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alisa M. Loosen ◽  
Vasilisa Skvortsova ◽  
Tobias U. Hauser
Keyword(s):  
Mental Health ◽  
Information Seeking ◽  
Large Scale ◽  
Psychiatric Symptoms ◽  
Longitudinal Change ◽  
Depression Symptoms ◽  
Obsessive Compulsive ◽  
Related Information ◽  
Pandemic Wave

AbstractIncreased mental-health symptoms as a reaction to stressful life events, such as the Covid-19 pandemic, are common. Critically, successful adaptation helps to reduce such symptoms to baseline, preventing long-term psychiatric disorders. It is thus important to understand whether and which psychiatric symptoms show transient elevations, and which persist long-term and become chronically heightened. At particular risk for the latter trajectory are symptom dimensions directly affected by the pandemic, such as obsessive–compulsive (OC) symptoms. In this longitudinal large-scale study (N = 406), we assessed how OC, anxiety and depression symptoms changed throughout the first pandemic wave in a sample of the general UK public. We further examined how these symptoms affected pandemic-related information seeking and adherence to governmental guidelines. We show that scores in all psychiatric domains were initially elevated, but showed distinct longitudinal change patterns. Depression scores decreased, and anxiety plateaued during the first pandemic wave, while OC symptoms further increased, even after the ease of Covid-19 restrictions. These OC symptoms were directly linked to Covid-related information seeking, which gave rise to higher adherence to government guidelines. This increase of OC symptoms in this non-clinical sample shows that the domain is disproportionately affected by the pandemic. We discuss the long-term impact of the Covid-19 pandemic on public mental health, which calls for continued close observation of symptom development.

scholarly journals Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

2020 ◽  
Vol 117 (32) ◽  
pp. 19061-19071 ◽  
Author(s):  
Samantha Joel ◽  
Paul W. Eastwick ◽  
Colleen J. Allison ◽  
Ximena B. Arriaga ◽  
Zachary G. Baker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Relationship Quality ◽  
Individual Difference ◽  
Large Scale ◽  
Well Being ◽  
Attachment Anxiety ◽  
Self Report ◽  
Individual Difference Variables ◽  
Partner Satisfaction

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

scholarly journals Denoising large-scale biological data using network filters

2021 ◽  
Author(s):  
Andrew J Kavran ◽  
Aaron Clauset
Keyword(s):  
Large Scale ◽  
Synthetic Data ◽  
Interaction Network ◽  
Learning Task ◽  
Biological Data ◽  
Data Sets ◽  
Proteomics Data ◽  
Life History Variation ◽  
Wide Range ◽  
Underlying Processes

Abstract Background: Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.Results: We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data.Conclusions: Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

scholarly journals Prospective predictors of sustainment of multiple EBPs in a system-driven implementation context: Examining sustained delivery based on administrative claims

2021 ◽  
Vol 2 ◽  
pp. 263348952110578
Author(s):  
Anna S. Lau ◽  
Teresa Lind ◽  
Mojdeh Motamedi ◽  
Joyce H. L. Lui ◽  
Mary Kuckertz ◽  
...  
Keyword(s):  
Large Scale ◽  
Organizational Context ◽  
Scale Up ◽  
Volume Ratio ◽  
Organizational Factors ◽  
Self Report ◽  
Administrative Claims ◽  
Sustained Delivery ◽  
Cross Sectional ◽  
Program Leaders

Background System-driven scale-up of multiple evidence-based practices (EBPs) is an increasingly common method used in public mental health to improve care. However, there are little data on the long-term sustained delivery of EBPs within these efforts, and previous studies have relied on retrospective self-report within cross-sectional studies. This study identified prospective predictors of sustained EBP delivery at the EBP-, therapist-, and organizational-levels using survey and administrative claims data within a large-scale system-driven implementation effort. Methods 777 therapists and 162 program leaders delivering at least one of six EBPs of interest completed surveys assessing perceptions of EBPs and organizational context. These surveys were linked to administrative data to examine prospective predictors of therapists’ EBP delivery over 33 months. Results Five of the six EBPs implemented showed sustained delivery in the system, with volume varying by EBP. Although total EBP claim volume per therapist decreased over time, the volume ratio (ratio of EBP-specific claims to total EBP and non-EBP claims) stayed relatively stable. Multilevel models revealed that EBPs that required consultation, had unstructured content, higher therapist self-efficacy with the EBP, and more positive program leader perceptions of the EBP were associated with greater sustained volume and volume ratio of the EBP. Therapists who were trained in fewer EBPs, who were unlicensed, and who worked in agencies rated by program leaders as lower on organizational staff autonomy and stress showed greater sustained EBP volume and volume ratio. Finally, more direct service hours per week provided by therapist predicted greater sustained EBP volume, but lower volume ratio. Conclusions The results point to the importance of EBP, therapist, and organizational factors that may be targeted in implementation strategies to promote the sustainment of EBPs.

Emergent constraints on the large scale atmospheric circulation and regional hydroclimate: do they still work in CMIP6 and how much can they actually constrain the future?

2021 ◽  
pp. 1-62
Author(s):  
Isla R. Simpson ◽  
Karen A. McKinnon ◽  
Frances V. Davenport ◽  
Martin Tingley ◽  
Flavio Lehner ◽  
...  
Keyword(s):  
Large Scale ◽  
Coupled Model ◽  
Internal Variability ◽  
Stationary Wave ◽  
Meridional Wind ◽  
Least Squares Regression ◽  
Enso Teleconnections ◽  
Projected Change ◽  
Out Of Sample ◽  
The Relationship

AbstractAn ‘emergent constraint’ (EC) is a statistical relationship, across a model ensemble, between a measurable aspect of the present day climate (the predictor) and an aspect of future projected climate change (the predictand). If such a relationship is robust and understood, it may provide constrained projections for the real world. Here, Coupled Model Intercomparison Project 6 (CMIP6) models are used to revisit several ECs that were proposed in prior model intercomparisons with two aims: (1) to assess whether these ECs survive the partial out-of-sample test of CMIP6 and (2) to more rigorously quantify the constrained projected change than previous studies. To achieve the latter, methods are proposed whereby uncertainties can be appropriately accounted for, including the influence of internal variability, uncertainty on the linear relationship, and the uncertainty associated with model structural differences, aside from those described by the EC. Both least squares regression and a Bayesian Hierarchical Model are used. Three ECs are assessed: (a) the relationship between Southern Hemisphere jet latitude and projected jet shift, which is found to be a robust and quantitatively useful constraint on future projections; (b) the relationship between stationary wave amplitude in the Pacific-North American sector and meridional wind changes over North America (with extensions to hydroclimate), which is found to be robust but improvements in the predictor in CMIP6 result in it no longer substantially constrains projected change in either circulation or hydroclimate; and (c) the relationship between ENSO teleconnections to California and California precipitation change, which does not appear to be robust when using historical ENSO teleconnections as the predictor.

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