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Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database
Resting-state functional magnetic resonance imaging (MRI) has been used to investigate the brain activity related to autism spectrum disorder (ASD). In this study, we applied information from a large-scale dataset, the Autism Brain Imaging Data Exchange (ABIDE), to clinical applications. We recruite...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6588618/ https://www.ncbi.nlm.nih.gov/pubmed/31227769 http://dx.doi.org/10.1038/s41598-019-45465-9 |
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author | Chen, Chia-Min Yang, Pinchen Wu, Ming-Ting Chuang, Tzu-Chao Huang, Teng-Yi |
author_facet | Chen, Chia-Min Yang, Pinchen Wu, Ming-Ting Chuang, Tzu-Chao Huang, Teng-Yi |
author_sort | Chen, Chia-Min |
collection | PubMed |
description | Resting-state functional magnetic resonance imaging (MRI) has been used to investigate the brain activity related to autism spectrum disorder (ASD). In this study, we applied information from a large-scale dataset, the Autism Brain Imaging Data Exchange (ABIDE), to clinical applications. We recruited 21 patients with ASD and 23 individuals with neurotypical development (TD). We applied ASD biomarkers derived from ABIDE datasets and subsequently investigated the relationship between the MRI biomarkers and indicators from clinical screening questionnaires, the social responsiveness scale (SRS), and the Swanson, Nolan, and Pelham Questionnaire IV. The results indicated that the biomarkers generated from the default mode and executive control networks significantly differed between the participants with ASD and TD. In particular, the biomarkers derived from the default mode network were negatively correlated with the raw scores and model factors of the SRS. In summary, this study transferred the efforts of the global autism research community to clinical applications and identified connectivity-based biomarkers in ASD. |
format | Online Article Text |
id | pubmed-6588618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65886182019-06-28 Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database Chen, Chia-Min Yang, Pinchen Wu, Ming-Ting Chuang, Tzu-Chao Huang, Teng-Yi Sci Rep Article Resting-state functional magnetic resonance imaging (MRI) has been used to investigate the brain activity related to autism spectrum disorder (ASD). In this study, we applied information from a large-scale dataset, the Autism Brain Imaging Data Exchange (ABIDE), to clinical applications. We recruited 21 patients with ASD and 23 individuals with neurotypical development (TD). We applied ASD biomarkers derived from ABIDE datasets and subsequently investigated the relationship between the MRI biomarkers and indicators from clinical screening questionnaires, the social responsiveness scale (SRS), and the Swanson, Nolan, and Pelham Questionnaire IV. The results indicated that the biomarkers generated from the default mode and executive control networks significantly differed between the participants with ASD and TD. In particular, the biomarkers derived from the default mode network were negatively correlated with the raw scores and model factors of the SRS. In summary, this study transferred the efforts of the global autism research community to clinical applications and identified connectivity-based biomarkers in ASD. Nature Publishing Group UK 2019-06-21 /pmc/articles/PMC6588618/ /pubmed/31227769 http://dx.doi.org/10.1038/s41598-019-45465-9 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chen, Chia-Min Yang, Pinchen Wu, Ming-Ting Chuang, Tzu-Chao Huang, Teng-Yi Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database |
title | Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database |
title_full | Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database |
title_fullStr | Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database |
title_full_unstemmed | Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database |
title_short | Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database |
title_sort | deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6588618/ https://www.ncbi.nlm.nih.gov/pubmed/31227769 http://dx.doi.org/10.1038/s41598-019-45465-9 |
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