Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) we...

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Autores principales:	Yongxia Zhou, Fang Yu, Timothy Duong
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ddbb3aaf02184626bce3a2c82755653f
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spelling	oai:doaj.org-article:ddbb3aaf02184626bce3a2c82755653f2021-11-18T08:15:58ZMultiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.1932-620310.1371/journal.pone.0090405https://doaj.org/article/ddbb3aaf02184626bce3a2c82755653f2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24922325/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.Yongxia ZhouFang YuTimothy DuongPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e90405 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yongxia Zhou Fang Yu Timothy Duong Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
description	This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.
format	article
author	Yongxia Zhou Fang Yu Timothy Duong
author_facet	Yongxia Zhou Fang Yu Timothy Duong
author_sort	Yongxia Zhou
title	Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
title_short	Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
title_full	Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
title_fullStr	Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
title_full_unstemmed	Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
title_sort	multiparametric mri characterization and prediction in autism spectrum disorder using graph theory and machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/ddbb3aaf02184626bce3a2c82755653f
work_keys_str_mv	AT yongxiazhou multiparametricmricharacterizationandpredictioninautismspectrumdisorderusinggraphtheoryandmachinelearning AT fangyu multiparametricmricharacterizationandpredictioninautismspectrumdisorderusinggraphtheoryandmachinelearning AT timothyduong multiparametricmricharacterizationandpredictioninautismspectrumdisorderusinggraphtheoryandmachinelearning
_version_	1718422016629932032

Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

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