ArticleThe European journal of neuroscience2018
Predicting age from cortical structure across the lifespan.
Article in The European journal of neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Cortical complexity in eating disorders: a systematic review and qualitative synthesis.European archives of psychiatry and clinical neuroscience · 2026Pooled it
- Cortical complexity estimation using fractal dimension: A systematic review of the literature on clinical and nonclinical samples.The European journal of neuroscience · 2022Pooled it
- Bias in Structural MRI Correlates of Delay Discounting due to Head Motion.Human brain mapping · 2026Article
- Fractal analysis of brain shape formation predicts age and genetic similarity in human newborns.Nature neuroscience · 2026Article
- Molecular architecture of the altered cortical complexity in autism.Molecular autism · 2025Article
- Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines.Brain informatics · 2024Article
- Theta-gamma-coupling as predictor of working memory performance in young and elderly healthy people.Molecular brain · 2024Article
- Age Prediction Using Resting-State Functional MRI.Neuroinformatics · 2024Article
- Cortical folding correlates to aging and Alzheimer's Disease's cognitive and CSF biomarkers.Scientific reports · 2024Article
- Article
- Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.Scientific data · 2024Article
- Fractal Dimension Analysis in Neurological Disorders: An Overview.Advances in neurobiology · 2024Review
- Editorial: Methods and application in fractal analysis of neuroimaging data.Frontiers in human neuroscience · 2024Article
- Age- and sex-related differences in cortical morphology and their relationships with cognitive performance in healthy middle-aged and older adults.Quantitative imaging in medicine and surgery · 2023Article
- Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis.Frontiers in human neuroscience · 2023Article
- Lower cortical thickness and increased brain aging in adults with cocaine use disorder.Frontiers in psychiatry · 2023Article
- A correlational study between microstructural, macrostructural and functional age-related changes in the human visual cortex.PloS one · 2023Article
- Age-associated sex and asymmetry differentiation in hemispheric and lobar cortical ribbon complexity across adulthood: A UK Biobank imaging study.Human brain mapping · 2023Article
- Sensory system-specific associations between brain structure and balance.Neurobiology of aging · 2022Article
- Brain-age estimation accuracy is significantly increased using multishell free-water reconstruction.Human brain mapping · 2022Article
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Abstract
Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.
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