{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T11:57:05Z","timestamp":1790683025448,"version":"4.1.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Radiol"],"DOI":"10.1007\/s00247-026-06789-7","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T11:05:02Z","timestamp":1790679902000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Methodological limitations of the BrainStructureAges pipeline from the volBrain platform: a brief report of the normative pediatric group"],"prefix":"10.1007","author":[{"given":"Ekaterina","family":"Korotkova","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofya","family":"Kulikova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2280-3096","authenticated-orcid":false,"given":"Olga","family":"Lvova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitrii","family":"Tarasov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexey","family":"Meshkov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Kovtun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"6789_CR1","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1109\/JBHI.2021.3083187","volume":"26","author":"I Beheshti","year":"2022","unstructured":"Beheshti I, Ganaie MA, Paliwal V, et al (2022) Predicting brain age using machine learning algorithms: a comprehensive evaluation. IEEE J Biomed Health Inform 26:1432\u20131440","journal-title":"IEEE J Biomed Health Inform"},{"key":"6789_CR2","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1038\/mp.2017.62","volume":"23","author":"JH Cole","year":"2018","unstructured":"Cole JH, Ritchie SJ, Bastin ME et al (2018) Brain age predicts mortality. Mol Psychiatry 23:1385\u20131392","journal-title":"Mol Psychiatry"},{"key":"6789_CR3","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.neuroimage.2019.06.017","volume":"200","author":"SM Smith","year":"2019","unstructured":"Smith SM, Vidaurre D, Alfaro-Almagro F, et al (2019) Estimation of brain age delta from brain imaging. Neuroimage 200:528\u2013539","journal-title":"Neuroimage"},{"key":"6789_CR4","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1016\/j.neuroimage.2012.08.001","volume":"63","author":"K Franke","year":"2012","unstructured":"Franke K, Luders E, May A, et al (2012) Brain maturation: predicting individual BrainAGE in children and adolescents using structural MRI. Neuroimage 63:1305\u20131312","journal-title":"Neuroimage"},{"key":"6789_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-42414-5","volume":"13","author":"D Griffiths-King","year":"2023","unstructured":"Griffiths-King D, Wood AG, Novak J (2023) Predicting \u201cBrainage\u201d in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning. Sci Rep 13:15591","journal-title":"Sci Rep"},{"key":"6789_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.1040087","volume":"13","author":"C Zhang","year":"2022","unstructured":"Zhang C, Yan B, Mutalifu N, et al (2022) Predicting the brain age of children with cerebral palsy using a two-dimensional convolutional neural networks prediction model without gray and white matter segmentation. Front Neurol 13:1040087","journal-title":"Front Neurol"},{"key":"6789_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2016.00030","volume":"10","author":"JV Manj\u00f3n","year":"2016","unstructured":"Manj\u00f3n JV, Coup\u00e9 P (2016) Volbrain: an online MRI brain volumetry system. Front Neuroinform 10:30","journal-title":"Front Neuroinform"},{"key":"6789_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.26558","volume":"45","author":"HD Nguyen","year":"2024","unstructured":"Nguyen HD, Cl\u00e9ment M, Mansencal B, Coup\u00e9 P (2024) Brain structure ages-a new biomarker for multi-disease classification. Hum Brain Mapp 45:e26558","journal-title":"Hum Brain Mapp"},{"key":"6789_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117026","volume":"219","author":"P Coup\u00e9","year":"2020","unstructured":"Coup\u00e9 P, Mansencal B, Cl\u00e9ment M, et al (2020) Assemblynet: a large ensemble of CNNs for 3D whole brain MRI segmentation. Neuroimage 219:117026","journal-title":"Neuroimage"},{"key":"6789_CR10","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","volume":"19","author":"DS Marcus","year":"2007","unstructured":"Marcus DS, Wang TH, Parker J, et al (2007) Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19:1498\u20131507","journal-title":"J Cogn Neurosci"},{"key":"6789_CR11","doi-asserted-by":"publisher","unstructured":"Korotkova EV, Kulikova SP, Meshkov AV, et al (2026). A surprising increase in the predicted brain age in people who had suffered a stroke in their childhood: a real result or a pitfall in the analysis of MRI scans? In: Kravets, A.G., Shcherbakov, M.V., Krishna Bal, B. (eds) Creativity in intelligent technologies and data science. CIT&DS 2025. Communications in Computer and Information Science, vol 2803. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-032-14674-8_46","DOI":"10.1007\/978-3-032-14674-8_46"},{"key":"6789_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2024.1496143","volume":"18","author":"E Dragendorf","year":"2024","unstructured":"Dragendorf E, B\u00fcltmann E, Wolff D (2024) Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations. Front Neuroinform 18:1496143","journal-title":"Front Neuroinform"},{"key":"6789_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/math11051229","volume":"11","author":"WH Lee","year":"2023","unstructured":"Lee WH (2023) The choice of machine learning algorithms impacts the association between brain-predicted age difference and cognitive function. Math 11:1229","journal-title":"Math"},{"key":"6789_CR14","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1002\/jdn.10259","volume":"83","author":"O Turamanlar","year":"2023","unstructured":"Turamanlar O, Kundakci YE, Saritas A, et al (2023) Automatic segmentation of the cerebellum using volBrain software in normal paediatric population. Int J Dev Neurosci 83:323\u2013332","journal-title":"Int J Dev Neurosci"},{"key":"6789_CR15","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s00429-026-03074-z","volume":"231","author":"S I\u015f\u0131klar","year":"2026","unstructured":"I\u015f\u0131klar S, Sa\u011flam D (2026) Age-related volumetric differences of hippocampal subfields in the developing brain: a retrospective MRI study. Brain Struct Funct 231:19","journal-title":"Brain Struct Funct"},{"key":"6789_CR16","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhae142","volume":"34","author":"S I\u015f\u0131klar","year":"2024","unstructured":"I\u015f\u0131klar S, Sa\u011flam D (2024) Volumetric analysis of age- and sex-related changes in the corpus striatum and thalamus in the 1-18 age group: a retrospective magnetic resonance imaging study. Cereb Cortex 34:bhae142","journal-title":"Cereb Cortex"},{"key":"6789_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-63222-7","volume":"16","author":"L Whitmore","year":"2025","unstructured":"Whitmore L, Beck D (2025) Current challenges and future directions for brain age prediction in children and adolescents. Nat Commun 16:7771","journal-title":"Nat Commun"},{"key":"6789_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcn.2024.101385","volume":"67","author":"EA Hodgdon","year":"2024","unstructured":"Hodgdon EA, Anderson R, Azzawi HA et al (2024) MRI morphometry of the anterior and posterior cerebellar vermis and its relationship to sensorimotor and cognitive functions in children. Dev Cogn Neurosci 67:101385","journal-title":"Dev Cogn Neurosci"},{"key":"6789_CR19","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/j.neuron.2010.08.040","volume":"67","author":"JN Giedd","year":"2010","unstructured":"Giedd JN, Rapoport JL (2010) Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron 67:728\u2013734","journal-title":"Neuron"},{"key":"6789_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2020.102229","volume":"26","author":"AG de Lange","year":"2020","unstructured":"de Lange AG, Cole JH (2020) Commentary: Correction procedures in brain-age prediction. NeuroImage Clinical 26:102229","journal-title":"NeuroImage Clinical"},{"key":"6789_CR21","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neurobiolaging.2020.03.014","volume":"92","author":"JH Cole","year":"2020","unstructured":"Cole JH (2020) Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging 92:34\u201342","journal-title":"Neurobiol Aging"},{"key":"6789_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.102063","volume":"24","author":"I Beheshti","year":"2019","unstructured":"Beheshti I, Nugent S, Potvin O, Duchesne S (2019) Bias-adjustment in neuroimaging-based brain age frameworks: a robust scheme. Neuroimage Clin 24:102063","journal-title":"Neuroimage Clin"},{"key":"6789_CR23","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41386-018-0111-z","volume":"44","author":"AN Kaczkurkin","year":"2019","unstructured":"Kaczkurkin AN, Raznahan A, Satterthwaite TD (2019) Sex differences in the developing brain: insights from multimodal neuroimaging. Neuropsychopharmacology 44:71\u201385","journal-title":"Neuropsychopharmacology"}],"container-title":["Pediatric Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-026-06789-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00247-026-06789-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-026-06789-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T11:05:03Z","timestamp":1790679903000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00247-026-06789-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":23,"alternative-id":["6789"],"URL":"https:\/\/doi.org\/10.1007\/s00247-026-06789-7","relation":{},"ISSN":["1432-1998"],"issn-type":[{"value":"1432-1998","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,29]]},"assertion":[{"value":"18 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Local Research Ethics Board of the Ural Federal University (protocol \u2116 4 from 20.09.2023). The study was conducted with appropriate ethical oversight and parental\/guardian consent.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Conflicts of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}