{"doi":"10.1101/2022.02.10.479868","title":"The midpoint of cortical thinning between late childhood and early adulthood differs between individuals and brain regions: Evidence from longitudinal modelling in a 12-wave neuroimaging sample","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Charting human brain maturation between childhood and adulthood is a fundamental prerequisite for understanding the rapid biological and psychological changes during human development. Two barriers have precluded the quantification of maturational trajectories: demands on data and demands on estimation. Using high-temporal resolution neuroimaging data of up to 12-waves in the HUBU cohort (\n                  <jats:italic>N</jats:italic>\n                  = 90, aged 7-21 years) we investigate changes in apparent cortical thickness across childhood and adolescence. Fitting a four-parameter logistic nonlinear random effects mixed model, we quantified the characteristic, s-shaped, trajectory of cortical thinning in adolescence. This approach yields biologically meaningful parameters, including the midpoint of cortical thinning (MCT), which corresponds to the age at which the cortex shows most rapid thinning - in our sample occurring, on average, at 14 years of age. These results show that, given suitable data and models, cortical maturation can be quantified with precision for each individual and brain region.\n                </jats:p>","journal":null,"year":null,"id":626693,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":642955,"name":"Kathrine Skak Madsen","orcid":"0000-0001-8884-5902","position":1,"is_corresponding":false},{"id":642953,"name":"Louise Baruël Johansen","orcid":"0000-0002-6312-9878","position":2,"is_corresponding":false},{"id":1621200,"name":"William FC Baaré","orcid":null,"position":3,"is_corresponding":false},{"id":257919,"name":"Rogier Kievit","orcid":"0000-0003-0700-4568","position":4,"is_corresponding":false},{"id":1131999,"name":"Delia Fuhrmann","orcid":"0000-0003-4678-8828","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"The midpoint of cortical thinning between late childhood and early adulthood differs between individuals and brain regions: Evidence from longitudinal modelling in a 12-wave neuroimaging sample","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Charting human brain maturation between childhood and adulthood is a fundamental prerequisite for understanding the rapid biological and psychological changes during human development. Two barriers have precluded the quantification of maturational trajectories: demands on data and demands on estimation. Using high-temporal resolution neuroimaging data of up to 12-waves in the HUBU cohort (\n                  <jats:italic>N</jats:italic>\n                  = 90, aged 7-21 years) we investigate changes in apparent cortical thickness across childhood and adolescence. Fitting a four-parameter logistic nonlinear random effects mixed model, we quantified the characteristic, s-shaped, trajectory of cortical thinning in adolescence. This approach yields biologically meaningful parameters, including the midpoint of cortical thinning (MCT), which corresponds to the age at which the cortex shows most rapid thinning - in our sample occurring, on average, at 14 years of age. These results show that, given suitable data and models, cortical maturation can be quantified with precision for each individual and brain region.\n                </jats:p>","is_dataset_classified":null,"base_score":2.302585092994046,"endowment":2.302585092994046,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4210956364","authors":[],"funders":[{"funder_name":"Independent Research Fund Denmark","grant_id":"unidentified","title":"unidentified"},{"funder_name":"European Commission","grant_id":"732592","title":"Healthy minds from 0-100 years: Optimising the use of European brain imaging cohorts"},{"funder_name":"UK Research and Innovation","grant_id":"ES/T015861/1","title":"Windows of vulnerability: Sensitive periods for social adversity in adolescence"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2022,"count":6},{"year":2023,"count":2},{"year":2024,"count":1}],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/02/10/2022.02.10.479868.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/02/10/2022.02.10.479868.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2022.02.10.479868","host_type":"publisher"},{"url":"https://doi.org/10.1101/2022.02.10.479868","host_type":"repository"},{"url":"https://doi.org/10.1016/j.neuroimage.2022.119507","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/35882270","host_type":""},{"url":"https://doaj.org/article/3d154ed5630d40a79b20b03181f03bd3","host_type":""},{"url":"https://hdl.handle.net/https://repository.ubn.ru.nl/handle/2066/253386","host_type":""},{"url":"https://repository.ubn.ru.nl//bitstream/handle/2066/253386/253386.pdf","host_type":""},{"url":"https://hdl.handle.net/2066/253386","host_type":""},{"url":"https://kclpure.kcl.ac.uk/portal/en/publications/8c7788fb-f207-45c4-8511-6e82d4f9171e","host_type":""},{"url":"https://kclpure.kcl.ac.uk/ws/files/179015305/1_s2.0_S1053811922006231_main.pdf","host_type":""},{"url":"https://www.scopus.com/pages/publications/85135299924","host_type":""},{"url":"https://doi.org/https://doi.org/10.1016/j.neuroimage.2022.119507","host_type":""}],"fields_of_study":["Functional Brain Connectivity Studies","Advanced Neuroimaging Techniques and Applications","Advanced MRI Techniques and Applications","0301 basic medicine","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":[],"keywords":["Neuroimaging","Thinning","Human brain","Psychology","Neuroscience","Cohort","Early childhood","Cortex (anatomy)","Trajectory","Developmental psychology","Biology","Mathematics","Statistics","Physics","Adult","570","Adolescent","Radboud University Medical Center","Brain maturation","Neurosciences. Biological psychiatry. Neuropsychiatry","Cortical thickness","618","Radboudumc 13: Stress-related disorders DCMN: Donders Center for Medical Neuroscience","Sex differences","Humans","Nonlinear mixed models","Longitudinal Studies","Child","Cerebral Cortex","Brain","Rostral anterior cingulate","Cerebral Cortical Thinning","Magnetic Resonance Imaging","Adolescence","Rostral middle frontal gyrus","Medical Neuroscience - Radboud University Medical Center","RC321-571"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T14:52:21.702865Z","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":[]}