Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity

Abstract Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering the discriminative patterns for patients....

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Autores principales:	Xun-Heng Wang, Yun Jiao, Lihua Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/87d04a3d1b984d05a12be105c90caa3f
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spelling	oai:doaj.org-article:87d04a3d1b984d05a12be105c90caa3f2021-12-02T15:08:16ZIdentifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity10.1038/s41598-018-30308-w2045-2322https://doaj.org/article/87d04a3d1b984d05a12be105c90caa3f2018-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-30308-whttps://doaj.org/toc/2045-2322Abstract Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering the discriminative patterns for patients. The dynamic functional connectivity of ADHD remains unclear. Towards this end, 100 children with ADHD and 140 normal controls were obtained from the ADHD-200 Consortium. The raw features were derived from the temporal variability between intrinsic connectivity networks (ICNs) as well as the demographic and covariate variables. The diagnostic model was based on the support vector machines (SVMs). The performance of diagnostic model was analyzed using leave-one-out cross-validation (LOOCV) and 10-folds cross-validations (CVs). The diagnostic model based on inter-ICN variability outperformed that based on inter-ICN functional connectivity and inter-ICN phase synchrony. The LOOCV achieved total accuracy of 78.75%, the sensitivity of 76%, and the specificity of 80.71%. The 10-folds CVs achieved average prediction accuracy of 75.54% ± 1.34%, average sensitivity of 70.5% ± 2.34%, and average specificity of 77.44% ± 1.47%. In addition, the discriminative patterns for ADHD were discovered using SVMs. The discriminative patterns confirmed with previous findings. In summary, individuals with ADHD could be identified through inter-ICN variability, which could be potential biomarkers for diagnostic model of ADHD.Xun-Heng WangYun JiaoLihua LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xun-Heng Wang Yun Jiao Lihua Li Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity
description	Abstract Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering the discriminative patterns for patients. The dynamic functional connectivity of ADHD remains unclear. Towards this end, 100 children with ADHD and 140 normal controls were obtained from the ADHD-200 Consortium. The raw features were derived from the temporal variability between intrinsic connectivity networks (ICNs) as well as the demographic and covariate variables. The diagnostic model was based on the support vector machines (SVMs). The performance of diagnostic model was analyzed using leave-one-out cross-validation (LOOCV) and 10-folds cross-validations (CVs). The diagnostic model based on inter-ICN variability outperformed that based on inter-ICN functional connectivity and inter-ICN phase synchrony. The LOOCV achieved total accuracy of 78.75%, the sensitivity of 76%, and the specificity of 80.71%. The 10-folds CVs achieved average prediction accuracy of 75.54% ± 1.34%, average sensitivity of 70.5% ± 2.34%, and average specificity of 77.44% ± 1.47%. In addition, the discriminative patterns for ADHD were discovered using SVMs. The discriminative patterns confirmed with previous findings. In summary, individuals with ADHD could be identified through inter-ICN variability, which could be potential biomarkers for diagnostic model of ADHD.
format	article
author	Xun-Heng Wang Yun Jiao Lihua Li
author_facet	Xun-Heng Wang Yun Jiao Lihua Li
author_sort	Xun-Heng Wang
title	Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity
title_short	Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity
title_full	Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity
title_fullStr	Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity
title_full_unstemmed	Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity
title_sort	identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/87d04a3d1b984d05a12be105c90caa3f
work_keys_str_mv	AT xunhengwang identifyingindividualswithattentiondeficithyperactivitydisorderbasedontemporalvariabilityofdynamicfunctionalconnectivity AT yunjiao identifyingindividualswithattentiondeficithyperactivitydisorderbasedontemporalvariabilityofdynamicfunctionalconnectivity AT lihuali identifyingindividualswithattentiondeficithyperactivitydisorderbasedontemporalvariabilityofdynamicfunctionalconnectivity
_version_	1718388199156350976

Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity

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