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Structural Covariance Networks in Children with Autism or ADHD

BACKGROUND: While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable hete...

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Autores principales: Bethlehem, R. A. I., Romero-Garcia, R., Mak, E., Bullmore, E. T., Baron-Cohen, S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5903412/
https://www.ncbi.nlm.nih.gov/pubmed/28633299
http://dx.doi.org/10.1093/cercor/bhx135
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author Bethlehem, R. A. I.
Romero-Garcia, R.
Mak, E.
Bullmore, E. T.
Baron-Cohen, S.
author_facet Bethlehem, R. A. I.
Romero-Garcia, R.
Mak, E.
Bullmore, E. T.
Baron-Cohen, S.
author_sort Bethlehem, R. A. I.
collection PubMed
description BACKGROUND: While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. METHOD: Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness. RESULTS: We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups. CONCLUSIONS: Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the groups are distinct, yet on others there is convergence.
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spelling pubmed-59034122018-04-18 Structural Covariance Networks in Children with Autism or ADHD Bethlehem, R. A. I. Romero-Garcia, R. Mak, E. Bullmore, E. T. Baron-Cohen, S. Cereb Cortex Original Articles BACKGROUND: While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. METHOD: Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness. RESULTS: We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups. CONCLUSIONS: Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the groups are distinct, yet on others there is convergence. Oxford University Press 2017-08 2017-06-13 /pmc/articles/PMC5903412/ /pubmed/28633299 http://dx.doi.org/10.1093/cercor/bhx135 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Bethlehem, R. A. I.
Romero-Garcia, R.
Mak, E.
Bullmore, E. T.
Baron-Cohen, S.
Structural Covariance Networks in Children with Autism or ADHD
title Structural Covariance Networks in Children with Autism or ADHD
title_full Structural Covariance Networks in Children with Autism or ADHD
title_fullStr Structural Covariance Networks in Children with Autism or ADHD
title_full_unstemmed Structural Covariance Networks in Children with Autism or ADHD
title_short Structural Covariance Networks in Children with Autism or ADHD
title_sort structural covariance networks in children with autism or adhd
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5903412/
https://www.ncbi.nlm.nih.gov/pubmed/28633299
http://dx.doi.org/10.1093/cercor/bhx135
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