ArticleFrontiers in aging neuroscience2018
Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study.
Article in Frontiers in aging neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Preserving brain health in aging: structural and biochemical benefits of water based resistance training, a randomized controlled trial.BMC geriatrics · 2026Trial
- Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major.iScience · 2026Article
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- Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration.GeroScience · 2026Article
- Multimodal connectivity-based cortical segmentation with graph neural networks.Frontiers in neuroscience · 2026Article
- Multimodal Connectivity-based Cortical Segmentation with Graph Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank.Human brain mapping · 2025Article
- An Update on Neuroaging on Earth and in Spaceflight.International journal of molecular sciences · 2025Review
- A review of artificial intelligence-based brain age estimation and its applications for related diseases.Briefings in functional genomics · 2025Review
- The utility of explainable AI for MRI analysis: Relating model predictions to neuroimaging features of the aging brain.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines.Brain informatics · 2024Article
- Why experimental variation in neuroimaging should be embraced.Nature communications · 2024Review
- Brain identification of IBS patients based on GBDT and multiple imaging techniques.Physical and engineering sciences in medicine · 2024Article
- Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits.Brain sciences · 2024Article
- Article
- Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment.Scientific reports · 2023Article
- Review
- MRI-based brain age prediction model for children under 3 years old using deep residual network.Brain structure & function · 2023Article
- A Skewed Loss Function for Correcting Predictive Bias in Brain Age Prediction.IEEE transactions on medical imaging · 2023Article
- Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study.Translational psychiatry · 2023Article
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7 authors.
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Abstract
Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45-91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.
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