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Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children

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 app...

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Autores principales: Menon, Sreevalsan S., Krishnamurthy, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652047/
https://www.ncbi.nlm.nih.gov/pubmed/34899225
http://dx.doi.org/10.3389/fninf.2021.742807
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author Menon, Sreevalsan S.
Krishnamurthy, K.
author_facet Menon, Sreevalsan S.
Krishnamurthy, K.
author_sort Menon, Sreevalsan S.
collection PubMed
description 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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spelling pubmed-86520472021-12-09 Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children Menon, Sreevalsan S. Krishnamurthy, K. Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2021-11-24 /pmc/articles/PMC8652047/ /pubmed/34899225 http://dx.doi.org/10.3389/fninf.2021.742807 Text en Copyright © 2021 Menon and Krishnamurthy. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Menon, Sreevalsan S.
Krishnamurthy, K.
Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children
title Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children
title_full Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children
title_fullStr Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children
title_full_unstemmed Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children
title_short Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children
title_sort multimodal ensemble deep learning to predict disruptive behavior disorders in children
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652047/
https://www.ncbi.nlm.nih.gov/pubmed/34899225
http://dx.doi.org/10.3389/fninf.2021.742807
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