{"doi":"10.1101/2021.04.08.21255068","title":"Growth charts for individualized evaluation of brain morphometry for preschool children","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Brain development from 1 to 6 years-of-age anchors the rapid development of a wide range of functional capabilities. However, quantitative growth charts of typical development during this age period are lacking, preventing the identification of early brain abnormalities. Here we characterize the time-dependent individual differences of cortical thickness and subcortical volume in 340 typically developing children and construct regional growth curves for these brain morphological measures. The growth curves reflect four types of time-dependence for cortical thickness and subcortical volume metrics. At the individual level, the growth curve model provides percentiles for each brain region’s cortical thickness or volume during ages 1 to 6, allowing for individualized inferences of brain developmental status relative to the same-age population. The growth curves further demonstrate clinical utility potentials by identifying children with developmental speech and language disorders, achieving high accuracies on data collected on both 1.5T and 3T scanners. 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 and generalizability.</jats:p>","journal":null,"year":null,"id":591685,"datarank":0.41006082576003233,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.16864513889491722,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.16864513889491722,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":4,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":488599,"name":"Jia Li","orcid":"0000-0003-1076-4496","position":1,"is_corresponding":false},{"id":1513868,"name":"Xiaoli Su","orcid":null,"position":2,"is_corresponding":false},{"id":1045567,"name":"Yang Hu","orcid":"0000-0003-3393-6844","position":3,"is_corresponding":false},{"id":1513869,"name":"Tianmei Liu","orcid":null,"position":4,"is_corresponding":false},{"id":1513870,"name":"Shaoqing Ni","orcid":null,"position":5,"is_corresponding":false},{"id":448963,"name":"Haifeng Li","orcid":"0000-0001-8541-279X","position":6,"is_corresponding":false},{"id":1231782,"name":"Xi-Nian Zuo","orcid":null,"position":7,"is_corresponding":false},{"id":1019781,"name":"Junfen Fu","orcid":"0000-0001-6405-1251","position":8,"is_corresponding":false},{"id":1512532,"name":"Ti-Fei Yuan","orcid":null,"position":9,"is_corresponding":false},{"id":1090347,"name":"Zhi Yang","orcid":"0000-0002-2222-2312","position":10,"is_corresponding":false},{"id":1513867,"name":"Hongxi Zhang","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Growth charts for individualized evaluation of brain morphometry for preschool children","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Brain development from 1 to 6 years-of-age anchors the rapid development of a wide range of functional capabilities. However, quantitative growth charts of typical development during this age period are lacking, preventing the identification of early brain abnormalities. Here we characterize the time-dependent individual differences of cortical thickness and subcortical volume in 340 typically developing children and construct regional growth curves for these brain morphological measures. The growth curves reflect four types of time-dependence for cortical thickness and subcortical volume metrics. At the individual level, the growth curve model provides percentiles for each brain region’s cortical thickness or volume during ages 1 to 6, allowing for individualized inferences of brain developmental status relative to the same-age population. The growth curves further demonstrate clinical utility potentials by identifying children with developmental speech and language disorders, achieving high accuracies on data collected on both 1.5T and 3T scanners. 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 and generalizability.</jats:p>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26657633","pmcid":null,"openalex_id":"https://openalex.org/W3154993722","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2021,"count":1},{"year":2022,"count":1},{"year":2023,"count":1},{"year":2024,"count":1}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2021/04/13/2021.04.08.21255068.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2021/04/13/2021.04.08.21255068.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2021.04.08.21255068","host_type":"publisher"},{"url":"https://doi.org/10.1101/2021.04.08.21255068","host_type":"repository"}],"fields_of_study":["Functional Brain Connectivity Studies","Language Development and Disorders","Congenital heart defects research"],"mesh_terms":[],"keywords":["Generalizability theory","Brain size","Brain development","Brain morphometry","Psychology","Growth curve (statistics)","Neuroscience","Developmental psychology","Audiology","Medicine","Econometrics","Mathematics","Magnetic resonance imaging","Radiology"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-26T10:13:03.022321Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}