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References from Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection. Local targets link to admitted publications; unresolved targets remain external evidence.
Predicting age using neuroimaging: innovative brain ageing biomarkers
10.1016/j.tins.2017.10.001 · 2017 · External reference
Machine learning for brain age prediction: Introduction to methods and clinical applications
10.1016/j.ebiom.2021.103600 · 2021 · External reference
Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics
10.1016/j.nicl.2022.103003 · 2022 · External reference
BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer’s disease
2013 · External reference
Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders
10.1093/schbul/sbt142 · 2014 · External reference
Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group
10.1038/s41380-020-0754-0 · 2021 · External reference
Brain age and other bodily “ages”: implications for neuropsychiatry
10.1038/s41380-018-0098-1 · 2019 · External reference
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UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
10.1371/journal.pmed.1001779 · 2015 · External reference
Some practical guidance for the implementation of propensity score matching
10.1111/j.1467-6419.2007.00527.x · 2008 · External reference
10.1007/978-0-387-84858-7
10.1007/978-0-387-84858-7 · 2009 · External reference
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
2021 · External reference
Do transformers and CNNs learn different concepts of brain age?
10.1002/hbm.70243 · 2025 · External reference
Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images.
10.1007/978-3-031-08999-2_22 · 2022 · External reference
Swin transformer: hierarchical vision transformer using shifted windows
2021 · External reference
Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank
10.1016/j.neuroimage.2017.10.034 · 2018 · External reference
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Brain atrophy in Alzheimer’s disease and aging
10.1016/j.arr.2016.01.002 · 2016 · External reference
Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group
10.1038/mp.2016.60 · 2017 · External reference
Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro imaging genetics through meta analysis (enigma) consortium
10.1016/j.biopsych.2018.04.023 · 2018 · External reference
Trajectories of brain aging in middle-aged and older adults: regional and individual differences
10.1016/j.neuroimage.2010.03.020 · 2010 · External reference
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Estimation of brain age delta from brain imaging
10.1016/j.neuroimage.2019.06.017 · 2019 · External reference
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Deep residual learning for image recognition
10.1109/cvpr.2016.90 · 2016 · External reference
Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
10.1038/s41598-020-76518-z · 2020 · External reference
Scikit-learn: machine learning in Python
2011 · External reference
