ArticleMedical & biological engineering & computing2014
Age estimation using cortical surface pattern combining thickness with curvatures.
Article in Medical & biological engineering & computing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis.Human brain mapping · 2026Pooled it
- Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank.Human brain mapping · 2025Article
- Accelerated brain aging in patients with major depressive disorder and its neurogenetic basis: evidence from neurotransmitters and gene expression profiles.Psychological medicine · 2025Article
- Article
- Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning.Nature and science of sleep · 2024Article
- Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Association of Maternal Depression During Pregnancy and Recent Stress With Brain Age Among Adult Offspring.JAMA network open · 2023Article
- Assessment of Characteristics of Imaging Biomarkers for Quantifying Anterior Cingulate Cortex Changes: A Twin Study of Middle- to Advanced-Aged Populations in East Asia.Medicina (Kaunas, Lithuania) · 2022Article
- Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration.NeuroImage · 2022Article
- Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation.Brain sciences · 2022Article
- Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.Molecular psychiatry · 2021Article
- Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.Human brain mapping · 2021Article
- Accelerated brain aging predicts impulsivity and symptom severity in depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Article
- Estimating Gender and Age from Brain Structural MRI of Children and Adolescents: A 3D Convolutional Neural Network Multitask Learning Model.Computational intelligence and neuroscience · 2021Article
- An automated machine learning approach to predict brain age from cortical anatomical measures.Human brain mapping · 2020Article
- Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction.Brain sciences · 2020Article
- Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019.Frontiers in psychiatry · 2020Article
- Brain age prediction using deep learning uncovers associated sequence variants.Nature communications · 2019Article
- Brain age and other bodily 'ages': implications for neuropsychiatry.Molecular psychiatry · 2019Review
- Interictal spike connectivity in human epileptic neocortex.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2019Article
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Authors and funding
6 authors.
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Abstract
Brain development and healthy aging have been proved to follow a specific pattern, which, in turn, can be applied to help doctors diagnose mental diseases. In this paper, we design a cortical surface pattern (CSP) combining the cortical thickness with curvatures, which constructs an accurate human age estimation model with relevance vector regression. We test our model with two public databases. One is the IXI database (360 healthy subjects aging from 20 to 82 years old were selected), and the other is the INDI database (303 subjects aging from 7 to 22 years old were selected). The results show that our model can achieve as small as 4.57 years deviation in the IXI database and 1.38 years deviation in the INDI database. Furthermore, we employ this surface pattern to age groups classification and get a remarkably high accuracy (97.77 %) and a significantly high sensitivity/specificity (97.30/98.10 %). These results suggest that our designed CSP combining thickness with curvatures is stable and sensitive to brain development, and it is much more powerful than voxel-based morphometry used in previous methods for age estimation.
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