ArticleScientific reports2017
Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- Viewing Pictures Triggers Rapid Morphological Enlargement in the Human Visual Cortex.Cerebral cortex (New York, N.Y. : 1991) · 2020Trial
- Neonatal brain age estimation from T2-weighted magnetic resonance images using shallow regression analysis.Scientific reports · 2026Article
- Current challenges and future directions for brain age prediction in children and adolescents.Nature communications · 2025Review
- Assessing the association between ADHD and brain maturation in late childhood and emotion regulation in early adolescence.Translational psychiatry · 2025Article
- Brain age prediction model based on electroencephalogram signal and its application in children with autism spectrum disorders.Frontiers in neurology · 2025Article
- Predicting 'Brainage' in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning.Scientific reports · 2023Article
- Predicting 'Brainage' in the Developmental Period using Structural MRI, Morphometric Similarity, and Machine Learning.Research square · 2023Article
- BrainAGE as a measure of maturation during early adolescence.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.Human brain mapping · 2022Article
- Accelerated brain aging predicts impulsivity and symptom severity in depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Article
- Multimodal Sparse Classifier for Adolescent Brain Age Prediction.IEEE journal of biomedical and health informatics · 2020Article
- Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence.Human brain mapping · 2019Article
- Accelerating research on biological aging and mental health: Current challenges and future directions.Psychoneuroendocrinology · 2019Review
- [A review on brain age prediction in brain ageing].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019Review
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4 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.