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Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children
OBJECTIVES: Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classif...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992191/ https://www.ncbi.nlm.nih.gov/pubmed/36908780 http://dx.doi.org/10.3389/fnins.2023.1138670 |
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author | Lin, Huang Haider, Stefan P. Kaltenhauser, Simone Mozayan, Ali Malhotra, Ajay Constable, R. Todd Scheinost, Dustin Ment, Laura R. Konrad, Kerstin Payabvash, Seyedmehdi |
author_facet | Lin, Huang Haider, Stefan P. Kaltenhauser, Simone Mozayan, Ali Malhotra, Ajay Constable, R. Todd Scheinost, Dustin Ment, Laura R. Konrad, Kerstin Payabvash, Seyedmehdi |
author_sort | Lin, Huang |
collection | PubMed |
description | OBJECTIVES: Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information. METHODS: From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI. RESULTS: Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes – most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580–0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables. CONCLUSION: Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics. |
format | Online Article Text |
id | pubmed-9992191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99921912023-03-09 Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children Lin, Huang Haider, Stefan P. Kaltenhauser, Simone Mozayan, Ali Malhotra, Ajay Constable, R. Todd Scheinost, Dustin Ment, Laura R. Konrad, Kerstin Payabvash, Seyedmehdi Front Neurosci Neuroscience OBJECTIVES: Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information. METHODS: From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI. RESULTS: Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes – most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580–0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables. CONCLUSION: Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics. Frontiers Media S.A. 2023-02-22 /pmc/articles/PMC9992191/ /pubmed/36908780 http://dx.doi.org/10.3389/fnins.2023.1138670 Text en Copyright © 2023 Lin, Haider, Kaltenhauser, Mozayan, Malhotra, Constable, Scheinost, Ment, Konrad and Payabvash. 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 Lin, Huang Haider, Stefan P. Kaltenhauser, Simone Mozayan, Ali Malhotra, Ajay Constable, R. Todd Scheinost, Dustin Ment, Laura R. Konrad, Kerstin Payabvash, Seyedmehdi Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children |
title | Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children |
title_full | Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children |
title_fullStr | Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children |
title_full_unstemmed | Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children |
title_short | Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children |
title_sort | population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992191/ https://www.ncbi.nlm.nih.gov/pubmed/36908780 http://dx.doi.org/10.3389/fnins.2023.1138670 |
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