Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation

Given the negative trajectories of early behavior problems associated with ADHD, early diagnosis is considered critical to enable intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, behavior...

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Main Authors: Ilke Öztekin, Mark A. Finlayson, Paulo A. Graziano, Anthony S. Dick
Format: Article
Language:English
Published: Elsevier 2021-06-01
Series:Developmental Cognitive Neuroscience
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1878929321000578
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spelling doaj-0757d8d6f11a419da5ea863b9580c53c2021-06-07T06:46:54ZengElsevierDevelopmental Cognitive Neuroscience1878-92932021-06-0149100966Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigationIlke Öztekin0Mark A. Finlayson1Paulo A. Graziano2Anthony S. Dick3Corresponding author at: Center for Children and Families, Florida International University, 11200 SW 8th St, Miami, FL, 33199, United States.; Florida International University, United StatesFlorida International University, United StatesFlorida International University, United StatesFlorida International University, United StatesGiven the negative trajectories of early behavior problems associated with ADHD, early diagnosis is considered critical to enable intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, behavioral and neural measures of executive function (EF) in predicting ADHD in a sample consisting of 162 young children (ages 4–7, mean age 5.55, 82.6 % Hispanic/Latino). Among the target measures, teacher ratings of EF were the most predictive of ADHD. While a more extensive evaluation of neural measures, such as diffusion-weighted imaging, may provide more information as they relate to the underlying cognitive deficits associated with ADHD, the current study indicates that measures of cortical anatomy obtained in research studies, as well cognitive measures of EF often obtained in routine assessments, have little incremental value in differentiating typically developing children from those diagnosed with ADHD. It is important to note that the overlap between some of the EF questions in the BRIEF, and the ADHD symptoms could be enhancing this effect. Thus, future research evaluating the importance of such measures in predicting children’s functional impairment in academic and social areas would provide additional insight into their contributing role in ADHD.http://www.sciencedirect.com/science/article/pii/S1878929321000578ADHDExecutive functionBrain imagingPattern analysisMachine learning
collection DOAJ
language English
format Article
sources DOAJ
author Ilke Öztekin
Mark A. Finlayson
Paulo A. Graziano
Anthony S. Dick
spellingShingle Ilke Öztekin
Mark A. Finlayson
Paulo A. Graziano
Anthony S. Dick
Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation
Developmental Cognitive Neuroscience
ADHD
Executive function
Brain imaging
Pattern analysis
Machine learning
author_facet Ilke Öztekin
Mark A. Finlayson
Paulo A. Graziano
Anthony S. Dick
author_sort Ilke Öztekin
title Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation
title_short Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation
title_full Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation
title_fullStr Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation
title_full_unstemmed Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation
title_sort is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of adhd in young children? a machine learning investigation
publisher Elsevier
series Developmental Cognitive Neuroscience
issn 1878-9293
publishDate 2021-06-01
description Given the negative trajectories of early behavior problems associated with ADHD, early diagnosis is considered critical to enable intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, behavioral and neural measures of executive function (EF) in predicting ADHD in a sample consisting of 162 young children (ages 4–7, mean age 5.55, 82.6 % Hispanic/Latino). Among the target measures, teacher ratings of EF were the most predictive of ADHD. While a more extensive evaluation of neural measures, such as diffusion-weighted imaging, may provide more information as they relate to the underlying cognitive deficits associated with ADHD, the current study indicates that measures of cortical anatomy obtained in research studies, as well cognitive measures of EF often obtained in routine assessments, have little incremental value in differentiating typically developing children from those diagnosed with ADHD. It is important to note that the overlap between some of the EF questions in the BRIEF, and the ADHD symptoms could be enhancing this effect. Thus, future research evaluating the importance of such measures in predicting children’s functional impairment in academic and social areas would provide additional insight into their contributing role in ADHD.
topic ADHD
Executive function
Brain imaging
Pattern analysis
Machine learning
url http://www.sciencedirect.com/science/article/pii/S1878929321000578
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