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:	Sreevalsan S. Menon, K. Krishnamurthy
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	deep learning disruptive behavior disorders multimodal ensemble learning neuroimaging 3D CNN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c08a8ef2ee404cedaaf349dfd04257c4
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spelling	oai:doaj.org-article:c08a8ef2ee404cedaaf349dfd04257c42021-11-30T17:57:56ZMultimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children1662-519610.3389/fninf.2021.742807https://doaj.org/article/c08a8ef2ee404cedaaf349dfd04257c42021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.742807/fullhttps://doaj.org/toc/1662-5196Oppositional 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.Sreevalsan S. MenonK. KrishnamurthyFrontiers Media S.A.articledeep learningdisruptive behavior disordersmultimodal ensemble learningneuroimaging3D CNNNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning disruptive behavior disorders multimodal ensemble learning neuroimaging 3D CNN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	deep learning disruptive behavior disorders multimodal ensemble learning neuroimaging 3D CNN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Sreevalsan S. Menon K. Krishnamurthy Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children
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.
format	article
author	Sreevalsan S. Menon K. Krishnamurthy
author_facet	Sreevalsan S. Menon K. Krishnamurthy
author_sort	Sreevalsan S. Menon
title	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_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_sort	multimodal ensemble deep learning to predict disruptive behavior disorders in children
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c08a8ef2ee404cedaaf349dfd04257c4
work_keys_str_mv	AT sreevalsansmenon multimodalensembledeeplearningtopredictdisruptivebehaviordisordersinchildren AT kkrishnamurthy multimodalensembledeeplearningtopredictdisruptivebehaviordisordersinchildren
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Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children

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