Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding

Abstract Brain development is a dynamic process with tissue-specific alterations that reflect complex and ongoing biological processes taking place during childhood and adolescence. Accurate identification and modelling of these anatomical processes in vivo with MRI may provide clinically useful ima...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Gareth Ball, Chris Adamson, Richard Beare, Marc L. Seal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5b020500dc8b4b46aa963a0de9fe85de
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5b020500dc8b4b46aa963a0de9fe85de
record_format	dspace
spelling	oai:doaj.org-article:5b020500dc8b4b46aa963a0de9fe85de2021-12-02T15:06:11ZModelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding10.1038/s41598-017-18253-62045-2322https://doaj.org/article/5b020500dc8b4b46aa963a0de9fe85de2017-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-18253-6https://doaj.org/toc/2045-2322Abstract Brain development is a dynamic process with tissue-specific alterations that reflect complex and ongoing biological processes taking place during childhood and adolescence. Accurate identification and modelling of these anatomical processes in vivo with MRI may provide clinically useful imaging markers of individual variability in development. In this study, we use manifold learning to build a model of age- and sex-related anatomical variation using multiple magnetic resonance imaging metrics. Using publicly available data from a large paediatric cohort (n = 768), we apply a multi-metric machine learning approach combining measures of tissue volume, cortical area and cortical thickness into a low-dimensional data representation. We find that neuroanatomical variation due to age and sex can be captured by two orthogonal patterns of brain development and we use this model to simultaneously predict age with a mean error of 1.5–1.6 years and sex with an accuracy of 81%. We validate this model in an independent developmental cohort. We present a framework for modelling anatomical development during childhood using manifold embedding. This model accurately predicts age and sex based on image-derived markers of cerebral morphology and generalises well to independent populations.Gareth BallChris AdamsonRichard BeareMarc L. SealNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gareth Ball Chris Adamson Richard Beare Marc L. Seal Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding
description	Abstract Brain development is a dynamic process with tissue-specific alterations that reflect complex and ongoing biological processes taking place during childhood and adolescence. Accurate identification and modelling of these anatomical processes in vivo with MRI may provide clinically useful imaging markers of individual variability in development. In this study, we use manifold learning to build a model of age- and sex-related anatomical variation using multiple magnetic resonance imaging metrics. Using publicly available data from a large paediatric cohort (n = 768), we apply a multi-metric machine learning approach combining measures of tissue volume, cortical area and cortical thickness into a low-dimensional data representation. We find that neuroanatomical variation due to age and sex can be captured by two orthogonal patterns of brain development and we use this model to simultaneously predict age with a mean error of 1.5–1.6 years and sex with an accuracy of 81%. We validate this model in an independent developmental cohort. We present a framework for modelling anatomical development during childhood using manifold embedding. This model accurately predicts age and sex based on image-derived markers of cerebral morphology and generalises well to independent populations.
format	article
author	Gareth Ball Chris Adamson Richard Beare Marc L. Seal
author_facet	Gareth Ball Chris Adamson Richard Beare Marc L. Seal
author_sort	Gareth Ball
title	Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding
title_short	Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding
title_full	Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding
title_fullStr	Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding
title_full_unstemmed	Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding
title_sort	modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/5b020500dc8b4b46aa963a0de9fe85de
work_keys_str_mv	AT garethball modellingneuroanatomicalvariationduringchildhoodandadolescencewithneighbourhoodpreservingembedding AT chrisadamson modellingneuroanatomicalvariationduringchildhoodandadolescencewithneighbourhoodpreservingembedding AT richardbeare modellingneuroanatomicalvariationduringchildhoodandadolescencewithneighbourhoodpreservingembedding AT marclseal modellingneuroanatomicalvariationduringchildhoodandadolescencewithneighbourhoodpreservingembedding
_version_	1718388584083357696

Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding

Ejemplares similares