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Aberrant brain dynamics and spectral power in children with ADHD and its subtypes
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, usually categorized as three subtypes, predominant inattention (ADHD-I), predominant hyperactivity-impulsivity (ADHD-HI), and a combined subtype (ADHD-C). Yet, common and unique abnormalities of e...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576687/ https://www.ncbi.nlm.nih.gov/pubmed/35996018 http://dx.doi.org/10.1007/s00787-022-02068-6 |
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author | Luo, Na Luo, Xiangsheng Zheng, Suli Yao, Dongren Zhao, Min Cui, Yue Zhu, Yu Calhoun, Vince D. Sun, Li Sui, Jing |
author_facet | Luo, Na Luo, Xiangsheng Zheng, Suli Yao, Dongren Zhao, Min Cui, Yue Zhu, Yu Calhoun, Vince D. Sun, Li Sui, Jing |
author_sort | Luo, Na |
collection | PubMed |
description | Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, usually categorized as three subtypes, predominant inattention (ADHD-I), predominant hyperactivity-impulsivity (ADHD-HI), and a combined subtype (ADHD-C). Yet, common and unique abnormalities of electroencephalogram (EEG) across different subtypes remain poorly understood. Here, we leveraged microstate characteristics and power features to investigate temporal and frequency abnormalities in ADHD and its subtypes using high-density EEG on 161 participants (54 ADHD-Is and 53 ADHD-Cs and 54 healthy controls). Four EEG microstates were identified. The coverage of salience network (state C) were decreased in ADHD compared to HC (p = 1.46e-3), while the duration and contribution of frontal–parietal network (state D) were increased (p = 1.57e-3; p = 1.26e-4). Frequency power analysis also indicated that higher delta power in the fronto-central area (p = 6.75e-4) and higher power of theta/beta ratio in the bilateral fronto-temporal area (p = 3.05e-3) were observed in ADHD. By contrast, remarkable subtype differences were found primarily on the visual network (state B), of which ADHD-C have higher occurrence and coverage than ADHD-I (p = 9.35e-5; p = 1.51e-8), suggesting that children with ADHD-C might exhibit impulsivity of opening their eyes in an eye-closed experiment, leading to hyper-activated visual network. Moreover, the top discriminative features selected from support vector machine model with recursive feature elimination (SVM-RFE) well replicated the above results, which achieved an accuracy of 72.7% and 73.8% separately in classifying ADHD and two subtypes. To conclude, this study highlights EEG microstate dynamics and frequency features may serve as sensitive measurements to detect the subtle differences in ADHD and its subtypes, providing a new window for better diagnosis of ADHD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00787-022-02068-6. |
format | Online Article Text |
id | pubmed-10576687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-105766872023-10-16 Aberrant brain dynamics and spectral power in children with ADHD and its subtypes Luo, Na Luo, Xiangsheng Zheng, Suli Yao, Dongren Zhao, Min Cui, Yue Zhu, Yu Calhoun, Vince D. Sun, Li Sui, Jing Eur Child Adolesc Psychiatry Original Contribution Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, usually categorized as three subtypes, predominant inattention (ADHD-I), predominant hyperactivity-impulsivity (ADHD-HI), and a combined subtype (ADHD-C). Yet, common and unique abnormalities of electroencephalogram (EEG) across different subtypes remain poorly understood. Here, we leveraged microstate characteristics and power features to investigate temporal and frequency abnormalities in ADHD and its subtypes using high-density EEG on 161 participants (54 ADHD-Is and 53 ADHD-Cs and 54 healthy controls). Four EEG microstates were identified. The coverage of salience network (state C) were decreased in ADHD compared to HC (p = 1.46e-3), while the duration and contribution of frontal–parietal network (state D) were increased (p = 1.57e-3; p = 1.26e-4). Frequency power analysis also indicated that higher delta power in the fronto-central area (p = 6.75e-4) and higher power of theta/beta ratio in the bilateral fronto-temporal area (p = 3.05e-3) were observed in ADHD. By contrast, remarkable subtype differences were found primarily on the visual network (state B), of which ADHD-C have higher occurrence and coverage than ADHD-I (p = 9.35e-5; p = 1.51e-8), suggesting that children with ADHD-C might exhibit impulsivity of opening their eyes in an eye-closed experiment, leading to hyper-activated visual network. Moreover, the top discriminative features selected from support vector machine model with recursive feature elimination (SVM-RFE) well replicated the above results, which achieved an accuracy of 72.7% and 73.8% separately in classifying ADHD and two subtypes. To conclude, this study highlights EEG microstate dynamics and frequency features may serve as sensitive measurements to detect the subtle differences in ADHD and its subtypes, providing a new window for better diagnosis of ADHD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00787-022-02068-6. Springer Berlin Heidelberg 2022-08-22 2023 /pmc/articles/PMC10576687/ /pubmed/35996018 http://dx.doi.org/10.1007/s00787-022-02068-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Contribution Luo, Na Luo, Xiangsheng Zheng, Suli Yao, Dongren Zhao, Min Cui, Yue Zhu, Yu Calhoun, Vince D. Sun, Li Sui, Jing Aberrant brain dynamics and spectral power in children with ADHD and its subtypes |
title | Aberrant brain dynamics and spectral power in children with ADHD and its subtypes |
title_full | Aberrant brain dynamics and spectral power in children with ADHD and its subtypes |
title_fullStr | Aberrant brain dynamics and spectral power in children with ADHD and its subtypes |
title_full_unstemmed | Aberrant brain dynamics and spectral power in children with ADHD and its subtypes |
title_short | Aberrant brain dynamics and spectral power in children with ADHD and its subtypes |
title_sort | aberrant brain dynamics and spectral power in children with adhd and its subtypes |
topic | Original Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576687/ https://www.ncbi.nlm.nih.gov/pubmed/35996018 http://dx.doi.org/10.1007/s00787-022-02068-6 |
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