Dataset Information

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.

SUBMITTER: Haller SP

PROVIDER: S-EPMC8096646 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Haller Simone P SP Stoddard Joel J Pagliaccio David D Bui Hong H MacGillivray Caroline C Jones Matt M Brotman Melissa A MA

Journal of the American Academy of Child and Adolescent Psychiatry 20201124 5

<h4>Objective</h4>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 ...[more]

PMID: 33242544

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Project description:ObjectiveDisruptive mood dysregulation disorder (DMDD), characterized by severe irritability, and attention-deficit/hyperactivity disorder (ADHD) are highly comorbid. This is the first study to characterize neural and behavioral similarities and differences in attentional functioning across these disorders.MethodTwenty-seven healthy volunteers, 31 patients with DMDD, and 25 patients with ADHD (8 to 18 years old) completed a functional magnetic resonance imaging attention task. Group differences in intra-subject variability in reaction time (RT) were examined. The present functional magnetic resonance imaging analytic approach precisely quantified trial-wise associations between RT and brain activity.ResultsGroup differences manifested in the relation between RT and brain activity (all regions: p < .01, F > 2.54, partial eta-squared [ηp2] > 0.06). Patients with DMDD showed specific alterations in the right paracentral lobule, superior parietal lobule, fusiform gyrus, and cerebellar culmen. In contrast, patients with DMDD and those with ADHD exhibited blunted compensatory increases in activity on long RT trials. In addition, youth with DMDD exhibited increased activity in the postcentral gyrus, medial frontal gyrus, and cerebellar tonsil and declive (all regions: p < .05, F > 2.46, ηp2 > 0.06). Groups in the imaging sample did not differ significantly in intra-subject variability in RT (F2,79 = 2.664, p = .076, ηp2 = 0.063), although intra-subject variability in RT was significantly increased in youth with DMDD and ADHD when including those not meeting strict motion and accuracy criteria for imaging analysis (F2,96 = 4.283, p = .017, ηp2 = 0.083).ConclusionPatients with DMDD exhibited specific alterations in the relation between pre-stimulus brain activity and RT. Patients with DMDD and those with ADHD exhibited similar blunting of compensatory neural activity in frontal, parietal, and other regions. In addition, patients with DMDD showed increased RT variability compared with healthy youth. This work is the first to identify common and unique behavioral and neural signatures of DMDD and ADHD.

Dataset Information

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

Objective

Method

Results

Conclusion

Publications

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

Similar Datasets

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