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

Abstract

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.

Trial registration: ClinicalTrials.gov NCT00006177 NCT00025935.

Keywords: ADHD; DMDD; attention; drift diffusion modeling; processing speed.

Published by Elsevier Inc.

Figures

Figure 1.. Quantile Probability Plot for Pooled Data From all Youth

Note: For each condition, the 0.1, 0.3, 0.5, 0.7, and 0.9 quantiles of reaction time are averaged across participants or simulations of their parameter set. Correct responses are on the right of each panel and represent almost all responses (average accuracy >92% by condition). Overall, the model reasonably represents behavior, especially in the neutral and congruent conditions. In the incongruent condition, the model underestimates the fastest 30% of reaction times, especially the fastest 10%. This is expected as attention dynamics that represent early interference effects on the drift rate are not modeled by a constant drift rate.

Figure 2.. Effects of diagnostic status and task condition on drift rate v, adjusted for IQ and Age

Note: For diagnostic codings, 1=Present and 0=Absent. Error bars reflect 95% CI. ADHD = attention dysregulation/hyperactivity disorder; DMDD = disruptive mood dysregulation disorder; HV = Healthy volunteers

Figure 3.. Effects of Dimensionally-Assessed Irritability and Inattention and Task Condition on Drift Rate v, Adjusted for IQ and Age.

Note: Error bars reflect 95% CI.