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, we examine the association between psychopathology (irritability and inattention) and processing efficiency under different attentional demands. Method: 187 youth with ADHD, DMDD, both disorders, or no major psychopathology (M age=13.09, SD=2.55; 34% female) completed an Eriksen Flanker task. Of these, 87 provided complete data on dimensional measures of the core symptom of DMDD, irritability, and those of ADHD, inattention and hyperactivity. Results: In a categorical analysis (n=187), we found a ADHD-by-DMDD-by task condition interaction on processing efficiency (b=-1.07, p=.01, 95%CI=[-1.89,-.24]), where increases in processing efficiency for non-conflict conditions were larger in youth without psychopathology relative to patients. Analysis of symptom severity (n=87) across diagnosis was consistent with the above analysis, revealing an interaction between symptom dimensions and task condition on processing efficiency (b=-0.10, p=.018, 95%CI[-.18, -.02]). Inattention, and its combined effect with irritability, predicted the magnitude of difference in processing efficiency between conflict and non-conflict conditions. Discussion: Reduced processing efficiency may represent a shared cognitive endophenotype between ADHD and DMDD. Youth high in irritability or inattention may have difficulty adjusting processing efficiency to changing task demands possibly reflecting impairments in cognitive flexibility.