scholarly journals Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modeling approach

2020 ◽  
Author(s):  
Ryan Smith ◽  
Namik Kirlic ◽  
Jennifer L. Stewart ◽  
James Touthang ◽  
Rayus Kuplicki ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Psychiatric Disorders ◽  
Clinical Sample ◽  
Negative Outcomes ◽  
Full Sample ◽  
Decision Uncertainty ◽  
Emotional Conflict ◽  
Model Free ◽  
Validation Parameters ◽  
Approach Avoidance

Background: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g. sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modeling to examine two factors often not distinguished within model-free analyses of AAC: decision uncertainty (DU) and sensitivity to negative outcomes vs. reward (emotional conflict; EC).Methods: A previously-validated AAC task was completed by 477 participants, including healthy controls (HCs; N=59), individuals with substance use disorders (SUDs; N=159) and individuals with depression and/or anxiety (DEP/ANX; N=260) disorders without SUDs. Using an active inference model, we estimated individual-level values for a model parameter (β) reflecting DU as well as another reflecting EC. Analyses were also repeated in a subsample propensity matched on age and general intelligence.Results: The model showed high accuracy (73%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. EC further correlated with self-reported anxiety during the task (r=0.32, p<0.001), while DU correlated with self-reported difficulty making decisions (r=0.45, p<0.001). Compared to HCs, both DEP/ANX and SUDs showed higher DU in the propensity matched sample (t=2.16, p = .03; and t=2.88, p = .005, respectively), with analogous results in the full sample; SUDs also showed lower EC in the full sample (t=3.17, p=0.002). Limitations: This study is limited by clinical sample heterogeneity and an inability to examine learning.Conclusions: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviors in psychiatric disorders.

scholarly journals Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

2020 ◽  
Vol 46 (1) ◽  
pp. E74-E87
Author(s):  
Ryan Smith ◽  
Namik Kirlic ◽  
Jennifer L. Stewart ◽  
James Touthang ◽  
Rayus Kuplicki ◽  
...  
Keyword(s):  
Substance Use ◽  
Anxiety Disorders ◽  
Psychiatric Disorders ◽  
Substance Use Disorders ◽  
Computational Modelling ◽  
Healthy Controls ◽  
Full Sample ◽  
Decision Uncertainty ◽  
Emotional Conflict ◽  
Approach Avoidance

Background: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). Methods: A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. Results: The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). Limitations: This study was limited by heterogeneity of the clinical sample and an inability to examine learning. Conclusion: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Sensitivity to expected negative outcomes during approach-avoidance conflict in a trans-diagnostic patient sample: a computational (active inference) modeling approach

2019 ◽  
Author(s):  
Ryan Smith ◽  
Namik Kirlic ◽  
Jennifer L. Stewart ◽  
James Touthang ◽  
Rayus Kuplicki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Reaction Times ◽  
Model Parameters ◽  
Active Inference ◽  
Negative Outcomes ◽  
Decision Uncertainty ◽  
Modeling Approach ◽  
Affective Stimuli ◽  
Approach Avoidance

Background: Sacrificing rewarding aspects of one’s life due to potential aversive outcomes is an important characteristic of multiple psychiatric disorders. Such decisions occur during approach-avoidance conflict (AAC), which has become the topic of a growing number of behavioral and neuroimaging studies. Here we describe a novel computational modeling approach to studying AAC.Methods: A previously-validated AAC task was completed by 479 participants including healthy controls (HCs), and individuals with depression, anxiety, and/or substance use disorders (SUDs), as part of the Tulsa 1000 study. An active inference model was utilized to identify parameters corresponding to the subjective aversiveness of affective stimuli (VNegative), the subjective value of points that could be won (VPoints), and decision uncertainty (β). We used correlational analyses to examine relationships to self-reported experiences during the task, analyses of variance to examine diagnostic group differences (depression/anxiety, substance use, HCs), and exploratory machine learning analyses to examine the contribution of dimensional clinical and neuropsychological measures.Results: Model parameters correlated with self-reported experience and reaction times during the task in expected directions. Relatve to HCs, both clinical groups showed higher VNegative values, and the SUD group exhibited less decision uncertainty (lower β values). Machine learning analyses highlighted several clinical domains (i.e., alcohol use, personality, working memory) potentially contributing to task parameters.Conclusions: Our results suggest that avoidance behavior in individuals with depression, anxiety, and SUDs may be driven by increased sensitivity to predicted negative outcomes and that insufficient decision uncertainty (overconfidence) may also further contribute to avoidance in substance use disorder.

scholarly journals Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryan Smith ◽  
Namik Kirlic ◽  
Jennifer L. Stewart ◽  
James Touthang ◽  
Rayus Kuplicki ◽  
...  
Keyword(s):  
Computational Modelling ◽  
Maladaptive Behavior ◽  
Group Differences ◽  
Decision Uncertainty ◽  
Emotional Conflict ◽  
Long Term Stability ◽  
Approach Avoidance ◽  
Uncertainty Parameters

AbstractMaladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2–3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

scholarly journals Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

2020 ◽  
Author(s):  
Ryan Smith ◽  
Namik Kirlic ◽  
Jennifer Stewart ◽  
James Touthang ◽  
Rayus Kuplicki ◽  
...  
Keyword(s):  
Computational Modelling ◽  
Maladaptive Behavior ◽  
Group Differences ◽  
Decision Uncertainty ◽  
Emotional Conflict ◽  
Long Term Stability ◽  
Approach Avoidance ◽  
Uncertainty Parameters

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes vs. reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. Here we analyzed 1-year follow-up data from a subset of the same participants (N=325) to assess parameter stability and relationships to other clinical and task measures. We also assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N=48), SUD (N=29), and DEP/ANX (N=121). Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46). Similar to previous baseline findings, these parameters correlated with multiple response time measures (ps&lt;.001) and self-reported anxiety (r=.30, p&lt;.001) and decision difficulty (r=.44, p&lt;.001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

Clinical features and comorbid psychiatric disorders in children and adolescents with trichotillomania: a clinical sample

2016 ◽  
Vol 17 (3) ◽  
pp. 248
Author(s):  
Hilal Adaletli ◽  
Hatice Gunes ◽  
Canan Tanidir ◽  
Caner Mutlu ◽  
Tugce Aytemiz ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Psychiatric Disorders ◽  
Clinical Features ◽  
Clinical Sample ◽  
Comorbid Psychiatric Disorders

scholarly journals Dopaminergic medication reduces striatal sensitivity to negative outcomes in Parkinson’s disease

2018 ◽  
Author(s):  
Brónagh McCoy ◽  
Sara Jahfari ◽  
Gwenda Engels ◽  
Tomas Knapen ◽  
Jan Theeuwes
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Learning Task ◽  
Dopamine Neurons ◽  
Negative Outcomes ◽  
Learning Differences ◽  
Dopaminergic Medication ◽  
Learning Rates ◽  
Approach Avoidance ◽  
Midbrain Dopamine

AbstractReduced levels of dopamine in Parkinson’s disease (PD) contribute to changes in learning, resulting from the loss of midbrain dopamine neurons that transmit a teaching signal to the striatum. Dopamine medication used by PD patients has previously been linked to either behavioral changes during learning itself or adjustments in approach and avoidance behavior after learning. To date, however, very little is known about the specific relationship between dopaminergic medication-driven differences during learning and subsequent changes in approach/avoidance tendencies in individual patients. We assessed 24 PD patients on and off dopaminergic medication and 24 healthy controls (HC) performing a probabilistic reinforcement learning task, while undergoing functional magnetic resonance imaging. During learning, medication in PD reduced an overemphasis on negative outcomes. When patients were on medication, learning rates were lower for negative (but not positive) outcomes and concurrent striatal BOLD responses showed reduced prediction error sensitivity. Medication-induced shifts in negative learning rates were predictive of changes in approach/avoidance choice patterns after learning, and these changes were accompanied by striatal BOLD response alterations. These findings highlight dopamine-driven learning differences in PD and provide new insight into how changes in learning impact the transfer of learned value to approach/avoidance responses in novel contexts.

scholarly journals Small Signal, Big Noise: Performance of the CIDI Depression Module

2005 ◽  
Vol 50 (13) ◽  
pp. 851-856 ◽  
Author(s):  
Paul A Kurdyak ◽  
William H Gnam
Keyword(s):  
Psychiatric Disorders ◽  
Clinical Sample ◽  
Community Sample ◽  
False Positive Rate ◽  
Test Validity ◽  
Community Setting ◽  
Composite International Diagnostic Interview ◽  
Critical Issue ◽  
Clinical Samples ◽  
Widespread Application

Objectives: With the release of data from the Canadian Community Health Survey: Mental Health and Weil-Being (Cycle 1.2), researchers have, for the first time, information on several psychiatric disorders from a nationally representative sample of Canadians residing in households. This survey used the Composite International Diagnostic Interview (CIDI) to identify persons with one or more psychiatric disorders. In this paper, our primary purpose was to evaluate the evidence supporting the validity of the CIDI—that is, the extent to which the depression diagnoses generated by the CIDI reflect true cases of depression. Method: We conducted a critical review of the CIDI, focusing on the depression module. Results: Reliability studies indicate that the CIDI performs reliably, as measured by interrater reliability. However, the use of different versions of the CIDI and the occasional exclusion of the Depression module from studies suggest that the reliability of the CIDI Depression module remains unconfirmed. The most critical issue in regard to the CIDI's performance is that clinical samples are used to test validity. A clinical sample has a higher prevalence of depression than a community sample. Conclusion: The results generated by the CIDI in a community setting likely will have a high false-positive rate, resulting in a falsely elevated prevalence rate. Given the widespread application of the CIDI internationally, addressing the outstanding concerns about validity with proper validation studies should become an international priority.

scholarly journals Behavioural Expressions, Imagery and Perfectionism

2011 ◽  
Vol 39 (4) ◽  
pp. 413-425 ◽  
Author(s):  
Michelle Lee ◽  
Cara Roberts-Collins ◽  
Anna Coughtrey ◽  
Lindsey Phillips ◽  
Roz Shafran
Keyword(s):  
Psychiatric Disorders ◽  
Mental Imagery ◽  
Clinical Sample ◽  
Mental Images ◽  
Axis I ◽  
Safety Behaviour ◽  
Initial Exploration

Background: High levels of multidimensional perfectionism may be dysfunctional in their own right and can also impact on the maintenance and treatment of Axis I psychiatric disorders. Aims: This paper sought to describe the behavioural expressions and imagery associated with perfectionism in a non-clinical sample. Method: Participants (n = 59) completed a newly developed questionnaire to assess behavioural expressions of perfectionism, and an adapted interview to assess perfectionism-related intrusive mental images. Results: The study found that those high in perfectionism took longer to complete tasks, experienced more checking and safety behaviour whilst carrying out tasks, and had greater trouble actually completing tasks compared to those low in perfectionism. In addition, those with higher levels of perfectionism experienced intrusive mental imagery, which was more distressing, harder to dismiss, and had more impact on behaviour than those with lower levels of perfectionism. Conclusions: This research provides an initial exploration of the specific behaviours and intrusive mental imagery associated with perfectionism. The new behavioural measure of perfectionism could prove useful clinically in the assessment of change; however, these findings are preliminary and warrant replication in a clinical sample in order to examine their treatment implications.

Bipolar Disorder in Children With ADHD: A Clinical Sample Study

2014 ◽  
Vol 21 (9) ◽  
pp. 715-720 ◽  
Author(s):  
Renato Donfrancesco ◽  
Michela Di Trani ◽  
Elda Andriola ◽  
Daniela Leone ◽  
Maria G. Torrioli ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Symptom Severity ◽  
Rating Scale ◽  
Clinical Sample ◽  
Pediatric Bipolar Disorder ◽  
Children With Adhd ◽  
Adhd Children ◽  
Adhd Subtype ◽  
The Impact

Objective: To explore the impact of early-onset bipolar disorder (pediatric bipolar disorder [PBD]) on ADHD. Method: We compared ADHD symptom severity, ADHD subtype distribution, and rates of comorbid and familial psychiatric disorders between 49 ADHD children with comorbid PBD and 320 ADHD children without PBD. Results: Children with ADHD and PBD showed higher scores in the Hyperactive and Inattentive subscales of the ADHD Rating Scale, than children with ADHD alone. The frequency of combined subtype was significantly higher in ADHD children with PBD, than in those with ADHD alone. ADHD children with PBD showed a higher rate of familial psychiatric disorders than ADHD children without PBD. The rate of conduct disorder was significantly greater in children with PBD and ADHD compared with children with ADHD alone. Conclusion: ADHD along with PBD presents with several characteristics that distinguish it from ADHD alone, suggesting that these may be distinct disorders.

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