Changes in guilt cognitions mediate the effect of trauma-informed guilt reduction therapy on PTSD and depression outcomes

OBJECTIVE

Trauma-informed guilt reduction therapy (TrIGR), a six-session cognitive behavioral therapy targeting trauma-related guilt and distress, reduces guilt and symptoms of posttraumatic stress disorder (PTSD) and depression, yet little is known regarding how and why TrIGR may be effective.

METHOD

This study examined treatment-related changes in avoidant coping and trauma-related guilt cognitions as possible mediators of treatment effects on PTSD and depression outcomes at 3- and 6-month follow-up. Data were from a randomized controlled trial for treatment of trauma-related guilt comparing TrIGR and supportive care therapy among 145 post-9/11 US veterans (Mage = 39.2 [8.1], 93.8% male).

RESULTS

At pretreatment, most (86%) met PTSD criteria. Intent to treat analyses using parallel mediation models indicated changes in guilt cognitions, but not avoidant coping, mediated the effect of TrIGR on reducing PTSD severity at 3-month (a × b = -0.15, p < 0.01, 95% CI: [-0.24 to -0.06], p = 0.001) and 6-month (a × b = -0.17, 95% CI: [-0.26 to -0.07], p = 0.001) follow-up. Similarly, changes in guilt cognitions, but not avoidant coping, mediated the effect of TrIGR on reducing depression severity at 3-month (a × b = -0.10, 95% CI: [-0.18 to -0.02], p = 0.02) and 6-month (a × b = -0.11, 95% CI: [-0.20 to -0.03], p = 0.01) follow-up.

CONCLUSIONS

Compared to guilt cognitions, changes in avoidant coping were less integral to downstream PTSD and depression symptom reduction. Guilt cognition change may be a salient active ingredient of PTSD and depression treatment for those with trauma-related guilt and a key therapy element to which providers should be attuned.

Motivational context and neurocomputation of stop expectation moderate early attention responses supporting proactive inhibitory control

Alterations in attention to cues signaling the need for inhibitory control play a significant role in a wide range of psychopathology. However, the degree to which motivational and attentional factors shape the neurocomputations of proactive inhibitory control remains poorly understood. The present study investigated how variation in monetary incentive valence and stake modulate the neurocomputational signatures of proactive inhibitory control. Adults (N = 46) completed a Stop-Signal Task (SST) with concurrent EEG recording under four conditions associated with stop performance feedback: low and high punishment (following unsuccessful stops) and low and high reward (following successful stops). A Bayesian learning model was used to infer individual's probabilistic expectations of the need to stop on each trial: P(stop). Linear mixed effects models were used to examine whether interactions between motivational valence, stake, and P(stop) parameters predicted P1 and N1 attention-related event-related potentials (ERPs) time-locked to the go-onset stimulus. We found that P1 amplitudes increased at higher levels of P(stop) in punished but not rewarded conditions, although P1 amplitude differences between punished and rewarded blocks were maximal on trials when the need to inhibit was least expected. N1 amplitudes were positively related to P(stop) in the high punishment condition (low N1 amplitude), but negatively related to P(stop) in the high reward condition (high N1 amplitude). Critically, high P(stop)-related N1 amplitude to the go-stimulus predicted behavioral stop success during the high reward block, providing evidence for the role of motivationally relevant context and inhibitory control expectations in modulating the proactive allocation of attentional resources that affect inhibitory control. These findings provide novel insights into the neurocomputational mechanisms underlying proactive inhibitory control under valence-dependent motivational contexts, setting the stage for developing motivation-based interventions that boost inhibitory control.

Comparison of Delivery of Care Before and During COVID-19 Within an Academic Outpatient Psychiatry Practice

Introduction: The COVID-19 pandemic has challenged outpatient mental health clinics. This article compares care delivery and patient characteristics before and during the COVID-19 pandemic in outpatient mental health clinics within an academic health system. Methods: A retrospective cohort study was conducted in patients who received outpatient psychiatric services at two clinics (A and B). The investigators compared care delivery with patients with mental health conditions prepandemic (January 1-December 31, 2019) and midpandemic (January 1-December 31, 2020) periods. Care delivery was defined as the number and type of new and return visits (telehealth and face-to-face visits), patients with recorded measurement-based care (MBC) outcomes, and communication capability between patients and providers. Results: During the prepandemic period, 6,984 patients were seen in Clinics A and B, resulting in 57,629 visits. In the midpandemic period, 7,110 patients were served, resulting in 61,766 total visits. Medication management visits increased from 2019 to 2020; number of visits with documented outcome measures increased by 90% in Clinic A and 15% in Clinic B. The number of MyChart messages per patient increased more than twofold during the midpandemic period. The number of new visits with primary diagnosis of anxiety disorders increased in CY2020 and the number of visits with primary diagnosis of major depressive/mood disorders decreased in CY2020. Payor mix did not vary between the two periods although there was variability between payor mix at the two primary clinic locations. Discussion: The study suggests that there was no detrimental impact on access to care between the prepandemic and midpandemic periods within the health system. Mental health visits while pivoting to telehealth increased during the midpandemic period. Transition to telepsychiatry improved the ability to administer and document MBC.

State-specific alterations in the neural computations underlying inhibitory control in women remitted from bulimia nervosa

The neurocomputational processes underlying bulimia nervosa and its primary symptoms, out-of-control overeating and purging, are poorly understood. Research suggests that the brains of healthy individuals form a dynamic internal model to predict whether control is needed in each moment. This study tested the hypothesis that this computational process of inhibitory control is abnormally affected by metabolic state (being fasted or fed) in bulimia nervosa. A Bayesian ideal observer model was fit to behavioral data acquired from 22 women remitted from bulimia nervosa and 20 group-matched controls who completed a stop-signal task during two counterbalanced functional MRI sessions, one after a 16 h fast and one after a meal. This model estimates participants' trial-by-trial updating of the probability of a stop signal based on their experienced trial history. Neural analyses focused on control-related Bayesian prediction errors, which quantify the direction and degree of "surprise" an individual experiences on any given trial. Regardless of group, metabolic state did not affect behavioral performance on the task. However, metabolic state modulated group differences in neural activation. In the fed state, women remitted from bulimia nervosa had attenuated prediction-error-dependent activation in the left dorsal caudate. This fed-state activation was lower among women with more frequent past binge eating and self-induced vomiting. When they are in a fed state, individuals with bulimia nervosa may not effectively process unexpected information needed to engage inhibitory control. This may explain the difficulties these individuals have stopping eating after it begins.

How Obstructed Action Efficacy Impacts Reward-based Decision-making in Adolescent Depression: An fMRI Study

OBJECTIVE

Disruption of reward seeking behavior by unforeseen obstacles can promote negative affect, including frustration and irritability, in adolescents. Repeated experiences of obstructed reward may in fact contribute to the development of depression in adolescents. However, the neurocognitive mechanisms whereby goal disruption impacts reward processing in adolescent depression have not yet been characterized. The present study addresses this gap using neuroimaging and a novel paradigm to assess how incidental action obstruction impacts reward-based decision-making.

METHOD

We assessed 62 unmedicated adolescents with Major Depressive Disorder (MDD; mean age=15.6, SD=1.4, 67% female participants) and 68 matched healthy control participants (mean age=15.3, SD=1.4, 50% female participants) using functional magnetic resonance imaging (fMRI) while they played a card game in which they had to guess between two options to earn points, in low- and high-stake conditions. Functioning of button presses through which they made decisions was intermittently blocked, thereby blocking action efficacy.

RESULTS

Participants with MDD made fewer button press repetitions in response to action efficacy obstruction, which was more apparent in the low-stake condition (Rate Ratio =0.85, p=0.034). During response repetition across stake conditions, MDDs exhibited higher activation in regions in the ventromedial prefrontal cortex, caudate, and putamen (F_(1,125)= 16.4-25.6, df=1,125; ps<0.001; Hedges' g=0.85-0.98).

CONCLUSION

Adolescent with depression tend to exhibit less flexible behavioral orientation in the face of blocked action efficacy, and abnormalities in neural systems critical to regulating negative affect during reward-based decision-making. This research highlights possible mechanisms relevant to understanding and treating affective dysregulation in adolescent depression.

Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy.

METHODS

In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment.

RESULTS

We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption.

CONCLUSIONS

Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions - setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

The effect of obstructed action efficacy on reward-based decision-making in healthy adolescents: a novel functional MRI task to assay frustration

Frustration is common in adolescence and often interferes with executive functioning, particularly reward-based decision-making, and yet very little is known about how incidental frustrating events (independent of task-based feedback) disrupt the neural circuitry of reward processing in this important age group. While undergoing functional magnetic resonance imaging (fMRI), 45 healthy adolescents played a card game in which they had to guess between two options to earn points, in low- and high-stake conditions. Functioning of button presses through which they made decisions was intermittently blocked, thereby increasing frustration potential. Neural deactivation of the precuneus, a Default Mode Network region, was observed during obstructed action blocks across stake conditions, but less so on high- relative to low-stake trials. Moreover, less deactivation in goal-directed reward processing regions (i.e., caudate), frontoparietal "task control" regions, and interoceptive processing regions (i.e., somatosensory cortex, thalamus) were observed on high-stake relative to low-stake trials. These findings are consistent with less disruption of goal-directed reward seeking during blocked action efficacy in high-stake conditions among healthy adolescents. These results provide a roadmap of neural systems critical to the processing of frustrating events during reward-based decision-making in youths and could help to characterize how frustration regulation is altered in a range of pediatric psychopathologies.

Inhibitory failures in cocaine use disorder: Not paying attention when there is a need to be cautious

BACKGROUND

Stimulant use disorders, such as cocaine use disorder, are associated with significant impairment in inhibitory control, which has in turn been linked to difficulties maintaining abstinence following treatment. Here, we combine the Dynamic Belief Model (DBM) and a Hierarchical Drift Diffusion Model (HDDM) to examine whether individuals with cocaine use disorder have both strategic response updating and tactical speed accuracy trade-off problems during inhibitory control.

METHODS

Twenty-seven individuals with cocaine use disorder and twenty-seven healthy control participants completed a Stop-Signal-Task (SST), in which one has to inhibit a motor response to a prepotent 2-alternative forced choice task on 25 % of the trials.

RESULTS

Cocaine use disorder and control subjects did not differ on successful stopping behavior. In cocaine use disorder but not control subjects, higher likelihood of encountering a stop signal was associated with lower drift rate. Moreover, in cocaine use disorder subjects, a more negative relationship between likelihood of encountering a stop signal and drift rate was associated with lower accuracy on stop trials and slower stop reaction time.

CONCLUSIONS

These results are consistent with a dysregulation between strategic and tactical processing during inhibitory control in cocaine use disorder. Specifically, these individuals are more likely to be less attentive to sensory evidence when the expectation of a stop signal is high.

Within-treatment clinical markers of dropout risk in integrated treatments for comorbid PTSD and alcohol use disorder

BACKGROUND

Integrated interventions for comorbid posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are effective, but many patients prematurely drop out from treatment. Little is known about within-treatment risk factors for dropout, limiting the ability during therapy to identify patients at risk for attrition.

METHODS

We examined measures assessing PTSD (PTSD Checklist for DSM-5; PCL-5), alcohol use (Substance Use Inventory; SUI), and patient satisfaction (Client Satisfaction Questionnaire; CSQ-8) as potential within-treatment markers of dropout risk, administered to 110 veterans in a randomized clinical trial of integrated exposure therapy versus integrated coping skills therapy for comorbid PTSD + AUD. Hierarchical Cox proportional hazard models with dropout status as the endpoint assessed effects of PCL-5, SUI, and CSQ-8 on dropout risk, and whether effects differed by treatment modality.

RESULTS

A significant interaction between treatment and changes in alcohol use was observed (HR = 2.86, p =  .007), where between-session alcohol use was positively associated with dropout hazard rate for those receiving integrated exposure therapy (HR = 2.34, p =  .004), but not coping skills therapy (HR = 0.73, p =  .19). Specifically, an increase of one drink consumed per day in the interval since last assessment (typically 2-3 weeks) was associated with a 5-fold increase in dropout hazard rate.

CONCLUSIONS

The findings provide preliminary evidence of detectable within-treatment markers of dropout during integrated treatment for PTSD + AUD. Study of within-treatment indicators proximal to dropout may help identify at-risk patients and inform timely strategies to boost retention.

Symptoms of Posttraumatic Stress Disorder are Associated with Exaggerated Neural Response to Surprising Errors

Posttraumatic stress disorder (PTSD) is characterized by exaggerated salience of previously innocuous cues and associated with hyperactivity of salience-related brain regions. Recently, computational models have been deployed to operationalize salience more precisely regarding surprise-driven learning, leading to findings that such learning is altered in anxiety-related disorders. In the present study, a sample of 20 combat veterans completed a probabilistic learning task during fMRI scanning. We applied a computational model to generate a trial-by-trial surprise signal (i.e., unsigned prediction error or difference between the expected probability of an outcome and the actual observed outcome), which allowed us to examine the neural response to surprising events. We did not observe an association between PTSD symptoms and behavioral indices of learning in the task. Surprising errors were associated with increased activity in the left precuneus/inferior parietal lobule and right inferior parietal lobule, two parietal regions that are linked to salience processing. Additionally, PTSD symptom severity was positively associated with precuneus/inferior parietal lobule activation to surprising errors, r = .63, p = .004. Taken together, this pattern of results suggests that PTSD symptoms are specifically associated with an exaggerated response to surprising errors in salience-related regions of the brain. This altered pattern of neural activity could represent a target for intervention to improve PTSD symptoms.

Higher affective congruency in the approach-avoidance task is associated with insular deactivation to dynamic facial expressions

Individuals exhibit a natural bias to approach positive social cues (e.g., smiling face) and to avoid negative ones, which may be altered in psychiatric conditions. Computerized approach/avoidance training to promote affectively congruent behavior has proven useful in modulating such biases. Here, we investigate how exposure to a higher rate of congruency impacts neural processing of social-affective cues. While undergoing functional magnetic resonance imaging (fMRI), twenty-four individuals completed two versions of the approach-avoidance task (AAT), in which they had to approach or avoid dynamic facial expressions of either happiness or disgust. In the high congruency condition, congruent responses (i.e. approaching happy faces, avoiding disgusted faces) were more frequent. The balanced condition had equal amounts of congruent and incongruent responses. Processing of congruent approach-avoidance actions towards social cues was associated with lower recruitment of the right anterior insula in the congruency-intensive relative to the balanced condition. Differential activation between the high congruency and balanced condition in the right hippocampus was negatively related to individuals' trait avoidance tendency. These findings are consistent with reduced affective neural processing of social cues when being exposed to congruent AAT contexts. These neural foci could be important targets when assessing the effectiveness of affective congruency training protocols.

Neural affective mechanisms associated with treatment responsiveness in veterans with PTSD and comorbid alcohol use disorder

Post-traumatic stress disorder (PTSD) is associated with neuro-physiological abnormalities reflecting increased anticipatory anxiety and reactivity to traumatic cues. It remains unclear whether neural mechanisms associated with PTSD treatment responsiveness, i.e. hyperactivation of the affective salience network in the brain, extend to a comorbid PTSD and substance use disorder population. Thirty-one Veterans with PTSD and co-occurring alcohol use disorder (AUD) were randomly assigned to either prolonged exposure or a non-exposure based treatment. They completed an affective anticipation task while undergoing fMRI, immediately prior and after completing treatment. After controlling for type and length of treatment, larger reduction of PTSD symptoms was associated with decreased anticipatory activation to negative trauma-related cues in the right pre-Supplementary Motor Area (pre-SMA), a region associated with emotion regulation. Smaller reduction in PTSD severity was associated with enhanced anticipatory activation to those cues within the right para-hippocampal region, an affective processing region. Our findings suggest that post-treatment reductions in anticipatory reactivity to trauma-related cues in the pre-SMA and para-hippocampal area are associated with larger PTSD symptom reduction in individuals with co-occurring PTSD and AUD. These results may offer neurofeedback training targets as an alternative to or enhancement of other PTSD treatment modalities in this population.

Neurocomputational Changes in Inhibitory Control Associated With Prolonged Exposure Therapy

Posttraumatic stress disorder (PTSD) is associated with inhibitory control dysfunction that extends beyond difficulties inhibiting trauma-related intrusions. Inhibitory learning has been proposed as a potential mechanism of change underlying the effectiveness of extinction-based therapies such as prolonged exposure (PE), a first-line treatment for PTSD. To identify neurocognitive markers of change in inhibitory learning associated with PE, we applied a Bayesian learning model to the analysis of neuroimaging data collected during an inhibitory control task, both before and after PE treatment. Veterans (N = 20) with combat-related PTSD completed a stop-signal task (SST) while undergoing fMRI at time points immediately before and after PE treatment. Participants exhibited a small, significant improvement in performance on the SST, as demonstrated by longer reaction times and improved inhibition accuracy. Amplitude of neural activation associated with a signed prediction error (SPE; i.e., the discrepancy between actual outcome and model-based expectation of needing to stop) in the right caudate decreased from baseline to posttreatment assessment. Change in model-based activation was modulated by performance accuracy, with a decrease in positive SPE activation observed on successful trials, d = 0.79, and a reduction in negative SPE activation on error trials, d = 0.74. The decrease in SPE-related activation on successful stop trials was correlated with PTSD symptom reduction. These results are consistent with the notion that PE may help broadly strengthen inhibitory learning and the development of more accurate model-based predictions, which may thus facilitate change in cognitions in response to trauma-related cues and help reduce PTSD symptoms.

Bayesian computational markers of relapse in methamphetamine dependence

Methamphetamine use disorder is associated with a high likelihood of relapse. Identifying robust predictors of relapse that have explanatory power is critical to develop secondary prevention based on a mechanistic understanding of relapse. Computational approaches have the potential to identify such predictive markers of psychiatric illness, with the advantage of providing a finer mechanistic explanation of the cognitive processes underlying psychiatric vulnerability.

In this study, sixty-two recently sober methamphetamine-dependent individuals were recruited from a 28-day inpatient treatment program, and completed a Stop Signal Task (SST) while undergoing functional magnetic resonance imaging (fMRI). These individuals were prospectively followed for 1 year and assessed for relapse to methamphetamine use. Thirty-three percent of followed participants reported relapse.

We found that neural activity associated with two types of Bayesian prediction error, i.e. the difference between actual and expected need to stop on a given trial, significantly differentiated those individuals who remained abstinent and those who relapsed. Specifically, relapsed individuals exhibited smaller neural activations to such Bayesian prediction errors relative to those individuals who remained abstinent in the left temporoparietal junction (Cohen's d = 0.91), the left inferior frontal gyrus (Cohen's d = 0.57), and left anterior insula (Cohen's d = 0.63). In contrast, abstinent and relapsed participants did not differ in neural activation to non-model based task contrasts or on various self-report clinical measures.

In conclusion, Bayesian cognitive models may help identify predictive biomarkers of relapse, while providing a computational explanation of belief processing and updating deficits in individuals with methamphetamine use disorder.

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Methamphetamine-dependent individuals (MDI) face a high rate of relapse after treatment.

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Can a Bayesian learning modeling and fMRI be used to identify markers of relapse?

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MDI who relapsed within 1 year have smaller activation to Bayesian model-based prediction errors.

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Such neural pattern was observed in left temporo-parietal junction, IFG, and anterior insula.

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MDI more likely to relapse show weaker tracking of uncertainty and updating of their belief model.

Anhedonia and anxiety underlying depressive symptomatology have distinct effects on reward-based decision-making

Depressive pathology, which includes both heightened negative affect (e.g., anxiety) and reduced positive affect (e.g., anhedonia), is known to be associated with sub-optimal decision-making, particularly in uncertain environments. Here, we use a computational approach to quantify and disambiguate how individual differences in these affective measures specifically relate to different aspects of learning and decision-making in reward-based choice behavior. Fifty-three individuals with a range of depressed mood completed a two-armed bandit task, in which they choose between two arms with fixed but unknown reward rates. The decision-making component, which chooses among options based on current expectations about reward rates, is modeled by two different decision policies: a learning-independent Win-stay/Lose-shift (WSLS) policy that ignores all previous experiences except the last trial, and Softmax, which prefers the arm with the higher expected reward. To model the learning component for the Softmax choice policy, we use a Bayesian inference model, which updates estimated reward rates based on the observed history of trial outcomes. Softmax with Bayesian learning better fits the behavior of 55% of the participants, while the others are better fit by a learning-independent WSLS strategy. Among Softmax “users”, those with higher anhedonia are less likely to choose the option estimated to be most rewarding. Moreover, the Softmax parameter mediates the inverse relationship between anhedonia and overall monetary gains. On the other hand, among WSLS “users”, higher state anxiety correlates with increasingly better ability of WSLS, relative to Softmax, to explain subjects’ trial-by-trial choices. In summary, there is significant variability among individuals in their reward-based, exploratory decision-making, and this variability is at least partly mediated in a very specific manner by affective attributes, such as hedonic tone and state anxiety.

Reduced Neural Recruitment for Bayesian Adjustment of Inhibitory Control in Methamphetamine Dependence

Delineating the processes that contribute to the progression and maintenance of substance dependence is critical to understanding and preventing addiction. Several previous studies have shown inhibitory control deficits in individuals with stimulant use disorder. We used a Bayesian computational approach to examine potential neural deficiencies in the dynamic predictive processing underlying inhibitory function among recently abstinent methamphetamine-dependent individuals (MDIs), a population at high risk of relapse. Sixty-two MDIs were recruited from a 28-day inpatient treatment program at the San Diego Veterans Affairs Medical Center and compared with 34 healthy control subjects. They completed a stop-signal task during functional magnetic resonance imaging. A Bayesian ideal observer model was used to predict individuals' trial-to-trial probabilistic expectations of inhibitory response, P(stop), to identify group differences specific to Bayesian expectation and prediction error computation. Relative to control subjects, MDIs were more likely to make stop errors on difficult trials and had attenuated slowing following stop errors. MDIs further exhibited reduced sensitivity as measured by the neural tracking of a Bayesian measure of surprise (unsigned prediction error), which was evident across all trials in the left posterior caudate and orbitofrontal cortex (Brodmann area 11), and selectively on stop error trials in the right thalamus and inferior parietal lobule. MDIs are less sensitive to surprising task events, both across trials and upon making commission errors, which may help explain why these individuals may not engage in switching strategy when the environment changes, leading to adverse consequences.

Executive Function in Previously Institutionalized Children

In studies of children adopted from institutions, being raised in an institution has been associated consistently with an increased risk of persistent cognitive, academic, and social-emotional problems. These findings raise questions about the neurocognitive mechanisms that contribute to these negative outcomes. Theory and models based on studies of animals indicate that development of the prefrontal cortex (PFC) and executive function (EF) may be particularly susceptible to environmental influences during early childhood. In this article, we review recent studies of postinstitutionalized children that examined EF components such as inhibitory control, working memory, shifting, and planning. We then describe emerging research on the structure and function of the PFC. Converging evidence suggests both EF difficulties and alterations in development of the PFC following early institutionalization. We conclude by discussing possible explanations for these findings and implications for prevention and intervention, and by offering suggestions for ongoing research.

Altered Statistical Learning and Decision-Making in Methamphetamine Dependence: Evidence from a Two-Armed Bandit Task

Understanding how humans weigh long-term and short-term goals is important for both basic cognitive science and clinical neuroscience, as substance users need to balance the appeal of an immediate high vs. the long-term goal of sobriety. We use a computational model to identify learning and decision-making abnormalities in methamphetamine-dependent individuals (MDI, n = 16) vs. healthy control subjects (HCS, n = 16), in a two-armed bandit task. In this task, subjects repeatedly choose between two arms with fixed but unknown reward rates. Each choice not only yields potential immediate reward but also information useful for long-term reward accumulation, thus pitting exploration against exploitation. We formalize the task as comprising a learning component, the updating of estimated reward rates based on ongoing observations, and a decision-making component, the choice among options based on current beliefs and uncertainties about reward rates. We model the learning component as iterative Bayesian inference (the Dynamic Belief Model), and the decision component using five competing decision policies: Win-stay/Lose-shift (WSLS), ε-Greedy, τ-Switch, Softmax, Knowledge Gradient. HCS and MDI significantly differ in how they learn about reward rates and use them to make decisions. HCS learn from past observations but weigh recent data more, and their decision policy is best fit as Softmax. MDI are more likely to follow the simple learning-independent policy of WSLS, and among MDI best fit by Softmax, they have more pessimistic prior beliefs about reward rates and are less likely to choose the option estimated to be most rewarding. Neurally, MDI's tendency to avoid the most rewarding option is associated with a lower gray matter volume of the thalamic dorsal lateral nucleus. More broadly, our work illustrates the ability of our computational framework to help reveal subtle learning and decision-making abnormalities in substance use.

The Influence of Depression on Cognitive Control: Disambiguating Approach and Avoidance Tendencies

Dysfunctions of approach and avoidance motivation play an important role in depression, which in turn may affect cognitive control, i.e., the ability to regulate thoughts and action to achieve internal goals. We use a novel experimental paradigm, i.e. a computer simulated driving-task, to study the impact of depression on cognitive control by measuring approach and avoidance actions in continuous time. In this task, 39 subjects with minimal to severe depression symptoms were instructed to use a joystick to move a virtual car as quickly as possible to a target point without crossing a stop-sign or crashing into a wall. We recorded their continuous actions on a joystick and found that depression 1) leads to further stopping distance to task target; and 2) increases the magnitude of late deceleration (avoidance) but not early acceleration (approach), which was only observed in the stop-sign condition. Taken together, these results are consistent with the hypothesis that depressed individuals have greater avoidance motivation near stopping target, but are minimally affected by approach motivation.

Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use

Bayesian ideal observer models quantify individuals' context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18-24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to 'stop' or to 'go' are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction.

Attenuated insular processing during risk predicts relapse in early abstinent methamphetamine-dependent individuals

There is some evidence that neuroimaging can be used to predict relapse among abstinent methamphetamine-dependent (MD) individuals. However, it remains unclear what cognitive and neural processes contribute to relapse. This investigation examined whether insula activation during risk-taking decisions-a process shown to be disrupted in MD-is able to predict susceptibility for relapse. Sixty-eight MD enrolled in a treatment program during early abstinence completed a risk-taking task during functional magnetic resonance imaging. Sixty-three of the sixty-eight individuals were followed up 1 year after the study. Of these, 18 MD reported relapse. The 45 abstinent MD showed patterns of insula activation during risky decisions that resembled those found in prior studies of healthy controls, consisting of lower insula activation during safe decisions paired with higher activation during risky decisions. In contrast, the 18 relapsed MD showed similar insula activation during safe and risky decisions. An increase in one standard deviation in the difference in insula activation between risky and safe choices was associated with a 0.34 odds ratio for relapse at any given time. A median split of insula activation (difference between risky and safe) showed that individuals in the bottom half were two times more likely to relapse. In addition, a model that included several other brain regions increased prediction accuracy compared with insula-based model alone. These results suggest that failure to differentially activate the insula as a function of risk is a part of an altered risk-processing network associated with an increased susceptibility to relapse.

The influence of emotions on cognitive control: feelings and beliefs-where do they meet?

The influence of emotion on higher-order cognitive functions, such as attention allocation, planning, and decision-making, is a growing area of research with important clinical applications. In this review, we provide a computational framework to conceptualize emotional influences on inhibitory control, an important building block of executive functioning. We first summarize current neuro-cognitive models of inhibitory control and show how Bayesian ideal observer models can help reframe inhibitory control as a dynamic decision-making process. Finally, we propose a Bayesian framework to study emotional influences on inhibitory control, providing several hypotheses that may be useful to conceptualize inhibitory control biases in mental illness such as depression and anxiety. To do so, we consider the neurocognitive literature pertaining to how affective states can bias inhibitory control, with particular attention to how valence and arousal may independently impact inhibitory control by biasing probabilistic representations of information (i.e., beliefs) and valuation processes (e.g., speed-error tradeoffs).

Social economic decision-making across the lifespan: An fMRI investigation

Recent research in neuroeconomics suggests that social economic decision-making may be best understood as a dual-systems process, integrating the influence of deliberative and affective subsystems. However, most of this research has focused on young adults and it remains unclear whether our current models extend to healthy aging. To address this question, we investigated the behavioral and neural basis of simple economic decisions in 18 young and 20 older healthy adults. Participants made decisions which involved accepting or rejecting monetary offers from human and non-human (computer) partners in an Ultimatum Game, while undergoing functional magnetic resonance imaging (fMRI). The partners' proposals involved splitting an amount of money between the two players, and ranged from $1 to $5 (from a $10 pot). Relative to young adults, older participants expected more equitable offers and rejected moderately unfair offers ($3) to a larger extent. Imaging results revealed that, relative to young participants, older adults had higher activations in the left dorsolateral prefrontal cortex (DLPFC) when receiving unfair offers ($1-$3). Age group moderated the relationship between left DLPFC activation and acceptance rates of unfair offers. In contrast, older adults showed lower activation of bilateral anterior insula in response to unfair offers. No age group difference was observed when participants received fair ($5) offers. These findings suggest that healthy aging may be associated with a stronger reliance on computational areas subserving goal maintenance and rule shifting (DLPFC) during interactive economic decision-making. Consistent with a well-documented "positivity effect", older age may also decrease recruitment of areas involved in emotion processing and integration (anterior insula) in the face of social norm violation.

The neural mechanisms of affect infusion in social economic decision-making: a mediating role of the anterior insula

Though emotions have been shown to have sometimes dramatic effects on decision-making, the neural mechanisms mediating these biases are relatively unexplored. Here, we investigated how incidental affect (i.e. emotional states unrelated to the decision at hand) may influence decisions, and how these biases are implemented in the brain. Nineteen adult participants made decisions which involved accepting or rejecting monetary offers from others in an Ultimatum Game while undergoing functional magnetic resonance imaging (fMRI). Prior to each set of decisions, participants watched a short video clip aimed at inducing either a sad or neutral emotional state. Results demonstrated that, as expected, sad participants rejected more unfair offers than those in the neutral condition. Neuroimaging analyses revealed that receiving unfair offers while in a sad mood elicited activity in brain areas related to aversive emotional states and somatosensory integration (anterior insula) and to cognitive conflict (anterior cingulate cortex). Sad participants also showed a diminished sensitivity in neural regions associated with reward processing (ventral striatum). Importantly, insular activation uniquely mediated the relationship between sadness and decision bias. This study is the first to reveal how subtle mood states can be integrated at the neural level to influence decision-making.

The impact of depression on social economic decision making

Although the role of emotion in social economic decision making has been increasingly recognized, the impact of mood disorders, such as depression, on such decisions has been surprisingly neglected. To address this gap, 15 depressed and 23 nondepressed individuals completed a well-known economic task, in which they had to accept or reject monetary offers from other players. Although depressed individuals reported a more negative emotional reaction to unfair offers, they accepted significantly more of these offers than did controls. A positive relationship was observed in the depressed group, but not in controls, between acceptance rates of unfair offers and resting cardiac vagal control, a physiological index of emotion regulation capacity. The discrepancy between depressed individuals' increased emotional reactions to unfair offers and their decisions to accept more of these offers contrasts with recent findings that negative mood in nondepressed individuals can lead to lower acceptance rates. This suggests distinct biasing processes in depression, which may be related to higher reliance on regulating negative emotion.