Differentiating neural sensitivity and bias during face-emotion processing in youth: a computational approach.

The ability to interpret face-emotion displays is critical for the development of adaptive social interactions. Using a novel variant of a computational model and fMRI data, we examined behavioral and neural associations between two metrics of face-emotion labeling (sensitivity and bias) and age in youth. Youth and adults (n = 44, M age = 20.02, s.d. = 7.44, range = 8-36) completed an explicit face-emotion labeling fMRI task including happy to angry morphed face emotions. A drift-diffusion model was applied to choice and reaction time distributions to examine sensitivity and bias in interpreting face emotions. Model fit and reliability of parameters were assessed on adult data (n = 42). Linear and quadratic slopes modeled brain activity associated with dimensions of face-emotion valence and ambiguity during interpretation. Behaviorally, age was associated with sensitivity. The bilateral anterior insula exhibited a more pronounced neural response to ambiguity with older age. Associations between sensitivity and bias metrics and activation patterns indicated that systems encoding face-emotion valence and ambiguity both contribute to the ability to discriminate face emotions. The current study provides evidence for age-related improvement in perceptual sensitivity to facial affect across adolescence and young adulthood.

A Randomized Controlled Trial of Computerized Interpretation Bias Training for Disruptive Mood Dysregulation Disorder: A Fast-Fail Study.

OBJECTIVE: To examine targeted, mechanism-based interventions is the next generation of treatment innovation. Biased threat labeling of ambiguous face emotions (interpretation bias) is a potential behavioral treatment target for anger, aggression, and irritability. Changing biases in face-emotion labeling may improve irritability-related outcomes. Here, we report the first randomized, double-blind, placebo-controlled targeted trial of interpretation bias training (IBT) in youths with chronic, severe irritability. METHOD: Patients with current disruptive mood dysregulation disorder (DMDD; N = 44) were randomly assigned to complete 4 sessions of active (n = 22) or sham (n = 22) computerized IBT training within a 1-week period. The first and last trainings were completed onsite, and 2 trainings were completed at home. We examined the effects of active IBT on labeling bias, primary outcome measures of irritability, and secondary outcome measures of anxiety, depression, and functional impairment. Follow-up assessments were completed immediately after the intervention as well as 1 and 2 weeks later. RESULTS: We found that active IBT engaged the behavioral target in the active relative to the sham condition, as shown by a significant shift toward labeling ambiguous faces as happy. However, there was no consistent clinical improvement in active IBT relative to the sham condition either immediately after or 2 weeks after training in either the primary or secondary outcome measures. CONCLUSION: Although this randomized controlled trial of IBT in youths with DMDD engaged the proposed behavioral target, there was no statistically significant improvement on clinical outcome. Identifying and changing behavioral targets is a first step in novel treatment development; these results have broader implications for target-based intervention development. CLINICAL TRIAL REGISTRATION INFORMATION: Psychological Treatments for Youth With Severe Irritability; https://clinicaltrials.gov/; NCT02531893.

Functional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth.

Irritability cuts across many pediatric disorders and is a common presenting complaint in child psychiatry; however, its neural mechanisms remain unclear. One core pathophysiological deficit of irritability is aberrant responses to frustrative nonreward. Here, we conducted a preliminary fMRI study to examine the ability of functional connectivity during frustrative nonreward to predict irritability in a transdiagnostic sample. This study included 69 youths (mean age = 14.55 years) with varying levels of irritability across diagnostic groups: disruptive mood dysregulation disorder (n = 20), attention-deficit/hyperactivity disorder (n = 14), anxiety disorder (n = 12), and controls (n = 23). During fMRI, participants completed a frustrating cognitive flexibility task. Frustration was evoked by manipulating task difficulty such that, on trials requiring cognitive flexibility, "frustration" blocks had a 50% error rate and some rigged feedback, while "nonfrustration" blocks had a 10% error rate. Frustration and nonfrustration blocks were randomly interspersed. Child and parent reports of the affective reactivity index were used as dimensional measures of irritability. Connectome-based predictive modeling, a machine learning approach, with tenfold cross-validation was conducted to identify networks predicting irritability. Connectivity during frustration (but not nonfrustration) blocks predicted child-reported irritability (ρ = 0.24, root mean square error = 2.02, p = 0.03, permutation testing, 1000 iterations, one-tailed). Results were adjusted for age, sex, medications, motion, ADHD, and anxiety symptoms. The predictive networks of irritability were primarily within motor-sensory networks; among motor-sensory, subcortical, and salience networks; and between these networks and frontoparietal and medial frontal networks. This study provides preliminary evidence that individual differences in irritability may be associated with functional connectivity during frustration, a phenotype-relevant state.

Computational Modeling of Attentional Impairments in Disruptive Mood Dysregulation and Attention-Deficit/Hyperactivity Disorder.

OBJECTIVE: Computational models provide information about cognitive components underlying behavior. When applied to psychopathology-relevant processes, they offer additional insight to observed differences in behavioral performance. Drift diffusion models have been successfully applied to investigate processing efficiency during binary choice tasks. Using these models, we examine the association between psychopathology (irritability and inattention/hyperactivity) and processing efficiency under different attentional demands. METHOD: A total of 187 youths with attention-deficit/hyperactivity disorder (ADHD), disruptive mood dysregulation disorder (DMDD), both disorders, or no major psychopathology (age, mean ± SD, 13.09 ± 2.55 y, 34% female) completed an Eriksen Flanker task. Of these, 87 youths provided complete data on dimensional measures of the core symptom of DMDD (irritability) and those of ADHD (inattention and hyperactivity). RESULTS: In a categorical diagnosis-based analysis (n = 187), we found significant interactive effects among ADHD, DMDD, and task condition on processing efficiency, whereby changes in processing efficiency between conflict and nonconflict conditions were larger in youths without psychopathology compared with patients. Analysis of symptom severity (n = 87) across diagnoses similarly revealed an interaction between symptom dimensions and task condition on processing efficiency. Irritability moderated the magnitude of association between inattention symptoms and difference in processing efficiency between conflict and nonconflict conditions. CONCLUSION: Adapting processing efficiency to cognitive demands may represent a shared cognitive endophenotype for both ADHD and DMDD. Highly irritable and/or inattentive youth may have difficulty adjusting processing efficiency to changing task demands, possibly reflecting impairments in cognitive flexibility.

A double-blind, randomized, placebo-controlled trial of a computer-based Interpretation Bias Training for youth with severe irritability: a study protocol.

BACKGROUND: Severe, chronic, and impairing irritability is a common presenting clinical problem in youth. Indeed, it was recently operationalized as disruptive mood dysregulation disorder (DMDD) in the DSM-5. However, to date, there are no evidence-based treatments that were specifically developed for DMDD. The current randomized controlled trial assesses the efficacy of a computer-based cognitive training intervention (Interpretation Bias Training; IBT) in youth with DMDD. IBT aims to reduce irritability by altering judgments of ambiguous face-emotions through computerized feedback. IBT is based on previous findings that youth with irritability-related psychopathology rate ambiguous faces as more hostile and fear producing. METHODS/DESIGN: This is a double-blind, randomized controlled trial of IBT in 40 youth with DMDD. Participants will be randomized to receive four IBT sessions (Active vs. Sham training) over 4 days. Active IBT provides computerized feedback to change ambiguous face-emotion interpretations towards happy interpretations. Face-emotion judgments are performed pre and post training, and for 2 weeks following training. Blinded clinicians will conduct weekly clinical ratings. Primary outcome measures assess changes in irritability using the clinician-rated Affective Reactivity Index (ARI) and Clinical Global Impressions-Improvement (CGI-I) scale for DMDD, as well as parent and child reports of irritability using the ARI. Secondary outcome measures include clinician ratings of depression, anxiety, and overall impairment. In addition, parent and child self-report measures of depression, anxiety, anger, social status, and aggression will be collected. DISCUSSION: The study described in this protocol will perform the first RCT testing the efficacy of IBT in reducing irritability in youth with DMDD. Developing non-pharmacological treatment options for youth suffering from severe, chronic irritability is important to potentially augment existing treatments. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT02531893 . Registered on 25 August 2015.

Putting our heads together: interpersonal neural synchronization as a biological mechanism for shared intentionality.

Shared intentionality, or collaborative interactions in which individuals have a shared goal and must coordinate their efforts, is a core component of human interaction. However, the biological bases of shared intentionality and, specifically, the processes by which the brain adjusts to the sharing of common goals, remain largely unknown. Using functional near infrared spectroscopy (fNIRS), coordination of cerebral hemodynamic activation was found in subject pairs when completing a puzzle together in contrast to a condition in which subjects completed identical but individual puzzles (same intention without shared intentionality). Interpersonal neural coordination was also greater when completing a puzzle together compared to two control conditions including the observation of another pair completing the same puzzle task or watching a movie with a partner (shared experience). Further, permutation testing revealed that the time course of neural activation of one subject predicted that of their partner, but not that of others completing the identical puzzle in different partner sets. Results indicate unique brain-to-brain coupling specific to shared intentionality beyond what has been previously found by investigating the fundamentals of social exchange.