Growth charts of brain morphometry for preschool children

Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of e...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:NeuroImage 2022-04, Vol.255, p.1-13
Hauptverfasser: Zhang, Hongxi, Li, Jia, Su, Xiaoli, Hu, Yang, Liu, Tianmei, Ni, Shaoqing, Li, Haifeng, Zuo, Xi-Nian, Fu, Junfen, Yuan, Ti-Fei, Yang, Zhi
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 13
container_issue
container_start_page 1
container_title NeuroImage
container_volume 255
creator Zhang, Hongxi
Li, Jia
Su, Xiaoli
Hu, Yang
Liu, Tianmei
Ni, Shaoqing
Li, Haifeng
Zuo, Xi-Nian
Fu, Junfen
Yuan, Ti-Fei
Yang, Zhi
description Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a sample size of 285, we characterized the age dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity. The brain growth charts are shared with the public (http://phi-group.top/resources.html).
format Article
fullrecord <record><control><sourceid>kuleuven</sourceid><recordid>TN_cdi_kuleuven_dspace_20_500_12942_702226</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>20_500_12942_702226</sourcerecordid><originalsourceid>FETCH-kuleuven_dspace_20_500_12942_7022263</originalsourceid><addsrcrecordid>eNqVykkOgjAUANAuNBGHO3RtovktoHRtHA7gvqlQUrTwyW9xuL0uPICu3uaNWCIgT1eFEGrCpiFcAUCJrEiYOhI-ouOlMxQDx5pfyDQdb5F6h62N9OI1Eu_JhtIh-s9sfEW2m7NxbXywi68ztjzsz7vT6jZ4O9xtp6vQm9JqCToH0EKqTOotSCk36Z95_XPW8RnTN3yZR3I</addsrcrecordid><sourcetype>Institutional Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Growth charts of brain morphometry for preschool children</title><source>Lirias (KU Leuven Association)</source><source>DOAJ Directory of Open Access Journals</source><source>Elsevier ScienceDirect Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Zhang, Hongxi ; Li, Jia ; Su, Xiaoli ; Hu, Yang ; Liu, Tianmei ; Ni, Shaoqing ; Li, Haifeng ; Zuo, Xi-Nian ; Fu, Junfen ; Yuan, Ti-Fei ; Yang, Zhi</creator><creatorcontrib>Zhang, Hongxi ; Li, Jia ; Su, Xiaoli ; Hu, Yang ; Liu, Tianmei ; Ni, Shaoqing ; Li, Haifeng ; Zuo, Xi-Nian ; Fu, Junfen ; Yuan, Ti-Fei ; Yang, Zhi</creatorcontrib><description>Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a sample size of 285, we characterized the age dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity. The brain growth charts are shared with the public (http://phi-group.top/resources.html).</description><identifier>ISSN: 1053-8119</identifier><language>eng</language><publisher>ACADEMIC PRESS INC ELSEVIER SCIENCE</publisher><ispartof>NeuroImage, 2022-04, Vol.255, p.1-13</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,315,776,780,27837</link.rule.ids></links><search><creatorcontrib>Zhang, Hongxi</creatorcontrib><creatorcontrib>Li, Jia</creatorcontrib><creatorcontrib>Su, Xiaoli</creatorcontrib><creatorcontrib>Hu, Yang</creatorcontrib><creatorcontrib>Liu, Tianmei</creatorcontrib><creatorcontrib>Ni, Shaoqing</creatorcontrib><creatorcontrib>Li, Haifeng</creatorcontrib><creatorcontrib>Zuo, Xi-Nian</creatorcontrib><creatorcontrib>Fu, Junfen</creatorcontrib><creatorcontrib>Yuan, Ti-Fei</creatorcontrib><creatorcontrib>Yang, Zhi</creatorcontrib><title>Growth charts of brain morphometry for preschool children</title><title>NeuroImage</title><description>Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a sample size of 285, we characterized the age dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity. The brain growth charts are shared with the public (http://phi-group.top/resources.html).</description><issn>1053-8119</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>FZOIL</sourceid><recordid>eNqVykkOgjAUANAuNBGHO3RtovktoHRtHA7gvqlQUrTwyW9xuL0uPICu3uaNWCIgT1eFEGrCpiFcAUCJrEiYOhI-ouOlMxQDx5pfyDQdb5F6h62N9OI1Eu_JhtIh-s9sfEW2m7NxbXywi68ztjzsz7vT6jZ4O9xtp6vQm9JqCToH0EKqTOotSCk36Z95_XPW8RnTN3yZR3I</recordid><startdate>20220423</startdate><enddate>20220423</enddate><creator>Zhang, Hongxi</creator><creator>Li, Jia</creator><creator>Su, Xiaoli</creator><creator>Hu, Yang</creator><creator>Liu, Tianmei</creator><creator>Ni, Shaoqing</creator><creator>Li, Haifeng</creator><creator>Zuo, Xi-Nian</creator><creator>Fu, Junfen</creator><creator>Yuan, Ti-Fei</creator><creator>Yang, Zhi</creator><general>ACADEMIC PRESS INC ELSEVIER SCIENCE</general><scope>FZOIL</scope></search><sort><creationdate>20220423</creationdate><title>Growth charts of brain morphometry for preschool children</title><author>Zhang, Hongxi ; Li, Jia ; Su, Xiaoli ; Hu, Yang ; Liu, Tianmei ; Ni, Shaoqing ; Li, Haifeng ; Zuo, Xi-Nian ; Fu, Junfen ; Yuan, Ti-Fei ; Yang, Zhi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-kuleuven_dspace_20_500_12942_7022263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Hongxi</creatorcontrib><creatorcontrib>Li, Jia</creatorcontrib><creatorcontrib>Su, Xiaoli</creatorcontrib><creatorcontrib>Hu, Yang</creatorcontrib><creatorcontrib>Liu, Tianmei</creatorcontrib><creatorcontrib>Ni, Shaoqing</creatorcontrib><creatorcontrib>Li, Haifeng</creatorcontrib><creatorcontrib>Zuo, Xi-Nian</creatorcontrib><creatorcontrib>Fu, Junfen</creatorcontrib><creatorcontrib>Yuan, Ti-Fei</creatorcontrib><creatorcontrib>Yang, Zhi</creatorcontrib><collection>Lirias (KU Leuven Association)</collection><jtitle>NeuroImage</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Hongxi</au><au>Li, Jia</au><au>Su, Xiaoli</au><au>Hu, Yang</au><au>Liu, Tianmei</au><au>Ni, Shaoqing</au><au>Li, Haifeng</au><au>Zuo, Xi-Nian</au><au>Fu, Junfen</au><au>Yuan, Ti-Fei</au><au>Yang, Zhi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Growth charts of brain morphometry for preschool children</atitle><jtitle>NeuroImage</jtitle><date>2022-04-23</date><risdate>2022</risdate><volume>255</volume><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>1053-8119</issn><abstract>Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a sample size of 285, we characterized the age dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity. The brain growth charts are shared with the public (http://phi-group.top/resources.html).</abstract><pub>ACADEMIC PRESS INC ELSEVIER SCIENCE</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1053-8119
ispartof NeuroImage, 2022-04, Vol.255, p.1-13
issn 1053-8119
language eng
recordid cdi_kuleuven_dspace_20_500_12942_702226
source Lirias (KU Leuven Association); DOAJ Directory of Open Access Journals; Elsevier ScienceDirect Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
title Growth charts of brain morphometry for preschool children
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T03%3A07%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-kuleuven&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Growth%20charts%20of%20brain%20morphometry%20for%20preschool%20children&rft.jtitle=NeuroImage&rft.au=Zhang,%20Hongxi&rft.date=2022-04-23&rft.volume=255&rft.spage=1&rft.epage=13&rft.pages=1-13&rft.issn=1053-8119&rft_id=info:doi/&rft_dat=%3Ckuleuven%3E20_500_12942_702226%3C/kuleuven%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true