Reward Processing in Children With Disruptive Behavior Disorders and Callous-Unemotional Traits in the ABCD Study

Hawes, Samuel W.; Waller, Rebecca; Byrd, Amy L.; Bjork, James M.; Dick, Anthony Steven; Riedel, Michael C.; Tobia, Michael J.; Thomson, Nicholas D.; Laird, Angela R.; González, Raúl

doi:10.1176/appi.ajp.2020.19101092

Cited by 31 publications

(40 citation statements)

References 37 publications

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“…Indeed, behavioral and neuroimaging studies in youth with DBD show performance deficits and differences in neural processing during paradigms that require behavioral modification in the context of competing reward and punishment (e.g., passive avoidance, response reversal; Byrd et al, 2014 , Blair et al, 2018 ). It is also possible that these individual differences are related to difficulties allocating attention to punishment that is less salient ( Patterson and Newman, 1993 ) and/or enhanced sensitivity to reward, as was previously demonstrated in this cohort ( Hawes et al, 2020 ). Taken together, findings from our large-scale investigation represent a critical step towards systematically addressing these questions, and highlight the need for continued work in this area.…”

Section: Discussionmentioning

confidence: 72%

Neural response to monetary loss among youth with disruptive behavior disorders and callous-unemotional traits in the ABCD study

Byrd

Hawes

Waller

et al. 2021

NeuroImage: Clinical

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Highlights We leveraged the ABCD Study to derive phenotypically narrow groups of youth with DBD in the largest loss processing functional MRI study to date. We examined neural activity to the anticipation and receipt of monetary loss using group, dimensional, and network-level analyses. There were no significant differences in brain activity to loss anticipation or receipt among youth with DBD with or without CU traits. Null findings were confirmed with and without covariates, using alternative grouping approaches, in dimensional models, and within network-level analyses.

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Section: Discussionmentioning

confidence: 72%

Neural response to monetary loss among youth with disruptive behavior disorders and callous-unemotional traits in the ABCD study

Byrd

Hawes

Waller

et al. 2021

NeuroImage: Clinical

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“…The data is available to qualified researchers at no cost after their NIMH Data Archive Data Use Certification has been approved. Children with DBDs were identified using the Child Behavior Checklist (CBCL) and the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version for DSM-5 (K-SADS-PL) (Hawes et al, 2020). Specifically, the criterion included children who: (i) scored at or above the borderline clinical range (i.e., T-scores ≥67) on either the CBCL DSM-oriented conduct problems subscale 1 https://dx.doi.org/10.15154/1504041 or oppositional defiant problems subscale; or (ii) received a K-SADS-PL conduct disorder or oppositional defiant disorder diagnosis.…”

Section: Datasetmentioning

confidence: 99%

“…Disruptive behavior disorders (DBDs) include oppositional defiant disorder (ODD; a pattern of angry/irritable mood, argumentative/defiant behavior, or vindictiveness lasting at least 6 months) and conduct disorder (CD; behavior in which the basic rights of others or major age-appropriate societal norms or rules are violated; American Psychiatric Association, 2013 ). They are prevalent in children and the most common reasons for referring children to mental health services (Hawes et al, 2020 ). ODD is estimated to occur in 2–16% of youth, depending on the population being studied and the method for diagnosis, and CD, which is more prevalent among younger males, rates range from 6 to 9% (SAMHSA, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, brain abnormalities are heterogeneous and cause alterations in functional connectivity and structural changes (McLaughlin et al, 2019 ). Studies have found abnormal brain activities in children with DBDs using dMRI (Hummer et al, 2015 ), sMRI (Wallace et al, 2014 ; Hummer et al, 2015 ; Waller et al, 2020 ), tfMRI (Rubia et al, 2009 ; Hawes et al, 2020 ), and rs-fMRI (Lu et al, 2015 ; Werhahn et al, 2020 ). Therefore, there is significant motivation to take advantage of complementary information on various aspects of neuropathology.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children

Menon

Krishnamurthy

2021

Front. Neuroinform.

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Oppositional defiant disorder and conduct disorder, collectively referred to as disruptive behavior disorders (DBDs), are prevalent psychiatric disorders in children. Early diagnosis of DBDs is crucial because they can increase the risks of other mental health and substance use disorders without appropriate psychosocial interventions and treatment. However, diagnosing DBDs is challenging as they are often comorbid with other disorders, such as attention-deficit/hyperactivity disorder, anxiety, and depression. In this study, a multimodal ensemble three-dimensional convolutional neural network (3D CNN) deep learning model was used to classify children with DBDs and typically developing children. The study participants included 419 females and 681 males, aged 108–131 months who were enrolled in the Adolescent Brain Cognitive Development Study. Children were grouped based on the presence of DBDs (n = 550) and typically developing (n = 550); assessments were based on the scores from the Child Behavior Checklist and on the Schedule for Affective Disorders and Schizophrenia for School-age Children-Present and Lifetime version for DSM-5. The diffusion, structural, and resting-state functional magnetic resonance imaging (rs-fMRI) data were used as input data to the 3D CNN. The model achieved 72% accuracy in classifying children with DBDs with 70% sensitivity, 72% specificity, and an F1-score of 70. In addition, the discriminative power of the classifier was investigated by identifying the cortical and subcortical regions primarily involved in the prediction of DBDs using a gradient-weighted class activation mapping method. The classification results were compared with those obtained using the three neuroimaging modalities individually, and a connectome-based graph CNN and a multi-scale recurrent neural network using only the rs-fMRI data.

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“…Methodologically, past fMRI studies in youths with ADHD and irritability have focused predominantly on regional, task-dependent neural activation (19). However, a multivariate approach focusing on coactivation and/or functional connectivity among regions may facilitate the discovery of brain-behavior associations and neurocognitive differences [ (26,27), see a review by Cooper et al (28)]. Therefore, the current study leveraged the large dataset (N = 11,875) from the Adolescent Brain Cognitive Development (ABCD) study at baseline (29,30) to identify differential neural correlates of cognitive control in youths with ADHD, irritability, and the co-occurrence of ADHD and irritability.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Neural Correlates of Response Inhibition and Error Processing in Children With Symptoms of Irritability and/or Attention-Deficit/Hyperactivity Disorder in the ABCD Study®

et al. 2022

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Attention-deficit/hyperactivity disorder (ADHD), characterized by symptoms of inattention and/or hyperactivity and impulsivity, is a neurodevelopmental disorder associated with executive dysfunctions, including response inhibition and error processing. Research has documented a common co-occurrence between ADHD and pediatric irritability. The latter is more characterized by affective symptoms, specifically frequent temper outbursts and low frustration tolerance relative to typically developing peers. Shared and non-shared neural correlates of youths with varied profiles of ADHD and irritability symptoms during childhood remain largely unknown. This study first classified a large sample of youths in the Adolescent Brain Cognitive Development (ABCD) study at baseline into distinct phenotypic groups based on ADHD and irritability symptoms (N = 11,748), and then examined shared and non-shared neural correlates of response inhibition and error processing during the Stop Signal Task in a subset of sample with quality neuroimaging data (N = 5,948). Latent class analysis (LCA) revealed four phenotypic groups, i.e., high ADHD with co-occurring irritability symptoms (n = 787, 6.7%), moderate ADHD with low irritability symptoms (n = 901, 7.7%), high irritability with no ADHD symptoms (n = 279, 2.4%), and typically developing peers with low ADHD and low irritability symptoms (n = 9,781, 83.3%). Latent variable modeling revealed group differences in the neural coactivation network supporting response inhibition in the fronto-parietal regions, but limited differences in error processing across frontal and posterior regions. These neural differences were marked by decreased coactivation in the irritability only group relative to youths with ADHD and co-occurring irritability symptoms and typically developing peers during response inhibition. Together, this study provided initial evidence for differential neural mechanisms of response inhibition associated with ADHD, irritability, and their co-occurrence. Precision medicine attending to individual differences in ADHD and irritability symptoms and the underlying mechanisms are warranted when treating affected children and families.

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Reward Processing in Children With Disruptive Behavior Disorders and Callous-Unemotional Traits in the ABCD Study

Cited by 31 publications

References 37 publications

Neural response to monetary loss among youth with disruptive behavior disorders and callous-unemotional traits in the ABCD study

Neural response to monetary loss among youth with disruptive behavior disorders and callous-unemotional traits in the ABCD study

Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children

Characterizing the Neural Correlates of Response Inhibition and Error Processing in Children With Symptoms of Irritability and/or Attention-Deficit/Hyperactivity Disorder in the ABCD Study®

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