An Experimental Investigation of Antisocial Lie‐Telling Among Children With Disruptive Behavior Disorders and Typically Developing Children

2017 ◽  
Vol 90 (3) ◽  
pp. 774-789 ◽  
Author(s):  
Allison P. Mugno ◽  
Lindsay C. Malloy ◽  
Daniel A. Waschbusch ◽  
William E. Pelham Jr. ◽  
Victoria Talwar
Keyword(s):  
Experimental Investigation ◽  
Disruptive Behavior ◽  
Behavior Disorders ◽  
Disruptive Behavior Disorders ◽  
Typically Developing ◽  
Typically Developing Children

scholarly journals Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children

2021 ◽  
Vol 15 ◽  
Author(s):  
Sreevalsan S. Menon ◽  
K. Krishnamurthy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Disruptive Behavior ◽  
Behavior Disorders ◽  
Child Behavior Checklist ◽  
Disruptive Behavior Disorders ◽  
Fmri Data ◽  
Typically Developing ◽  
Oppositional Defiant ◽  
3D Cnn

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.

Disruptive behavior disorders influence treatment response among adolescent marijuana abusers

2008 ◽  
Author(s):  
Jennifer J. Vanscoyoc ◽  
Catherine Stanger ◽  
Alan J. Budney ◽  
Jeff D. Thostenson
Keyword(s):  
Treatment Response ◽  
Disruptive Behavior ◽  
Behavior Disorders ◽  
Disruptive Behavior Disorders

Anxiety Symptom Profiles in Low-Income Urban Youth with Disruptive Behavior Disorders

2010 ◽  
Author(s):  
Jaleel Abdul-Adil ◽  
David A. Meyerson ◽  
Corinn Elmore ◽  
A. David Farmer ◽  
Karen Taylor-Crawford
Keyword(s):  
Disruptive Behavior ◽  
Low Income ◽  
Urban Youth ◽  
Behavior Disorders ◽  
Anxiety Symptom ◽  
Disruptive Behavior Disorders ◽  
Symptom Profiles

Witnessing or Knowledge of an Arrest and Disruptive Behavior Disorders: Longitudinal Findings in a Latino Group

2011 ◽  
Author(s):  
Lillian Polanco ◽  
Marjorine Henriquez ◽  
Kimberly Mantilla ◽  
Perla Corredor ◽  
Jacqueline Rodriguez ◽  
...  
Keyword(s):  
Disruptive Behavior ◽  
Behavior Disorders ◽  
Disruptive Behavior Disorders

scholarly journals Neuroimaging findings in disruptive behavior disorders

CNS Spectrums ◽  
2015 ◽  
Vol 20 (4) ◽  
pp. 369-381 ◽  
Author(s):  
Rosalind H. Baker ◽  
Roberta L. Clanton ◽  
Jack C. Rogers ◽  
Stéphane A. De Brito
Keyword(s):  
Antisocial Behavior ◽  
Disruptive Behavior ◽  
Behavior Disorders ◽  
Disruptive Behavior Disorders ◽  
Structural Connectivity ◽  
Cu Traits ◽  
Affective Stimuli ◽  
Imaging Research ◽  
Emotional Face

Decades of research have shown that youths with disruptive behavior disorders (DBD) are a heterogeneous population. Over the past 20 years, researchers have distinguished youths with DBD as those displaying high (DBD/HCU) versus low (DBD/LCU) callous-unemotional (CU) traits. These traits include flat affect and reduced empathy and remorse, and are associated with more severe, varied, and persistent patterns of antisocial behavior and aggression. Conduct problems in youths with HCU and LCU are thought to reflect distinct causal vulnerabilities, with antisocial behavior in youths with DBD/HCU reflecting a predominantly genetic etiology, while antisocial behavior in youths with DBD/LCU is associated primarily with environmental influences. Here we selectively review recent functional (fMRI) and structural (sMRI) magnetic resonance imaging research on DBD, focusing particularly on the role of CU traits. First, fMRI studies examining the neural correlates of affective stimuli, emotional face processing, empathy, theory of mind, morality, and decision-making in DBD are discussed. This is followed by a review of the studies investigating brain structure and structural connectivity in DBD. Next, we highlight the need to further investigate females and the role of sex differences in this population. We conclude the review by identifying potential clinical implications of this research.

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