MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide
Deep learning has emerged as a powerful approach to constructing imaging signatures of
normal brain ageing as well as of various neuropathological processes associated with …
normal brain ageing as well as of various neuropathological processes associated with …
Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging
Numerous studies have established that estimated brain age constitutes a valuable
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …
[HTML][HTML] Local brain-age: a U-net model
We propose a new framework for estimating neuroimaging-derived “brain-age” at a local
level within the brain, using deep learning. The local approach, contrary to existing global …
level within the brain, using deep learning. The local approach, contrary to existing global …
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Abstract Machine learning analysis of neuroimaging data can accurately predict
chronological age in healthy people. Deviations from healthy brain ageing have been …
chronological age in healthy people. Deviations from healthy brain ageing have been …
[HTML][HTML] Deep neural networks learn general and clinically relevant representations of the ageing brain
The discrepancy between chronological age and the apparent age of the brain based on
neuroimaging data—the brain age delta—has emerged as a reliable marker of brain health …
neuroimaging data—the brain age delta—has emerged as a reliable marker of brain health …
[HTML][HTML] Openbhb: a large-scale multi-site brain mri data-set for age prediction and debiasing
Prediction of chronological age from neuroimaging in the healthy population is an important
issue because the deviations from normal brain age may highlight abnormal trajectories …
issue because the deviations from normal brain age may highlight abnormal trajectories …
[HTML][HTML] Accurate brain‐age models for routine clinical MRI examinations
DA Wood, S Kafiabadi, A Al Busaidi, E Guilhem… - Neuroimage, 2022 - Elsevier
Convolutional neural networks (CNN) can accurately predict chronological age in healthy
individuals from structural MRI brain scans. Potentially, these models could be applied …
individuals from structural MRI brain scans. Potentially, these models could be applied …
[HTML][HTML] Learning patterns of the ageing brain in MRI using deep convolutional networks
Both normal ageing and neurodegenerative diseases cause morphological changes to the
brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally …
brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally …
Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan
Brain age estimated by machine learning from T1-weighted magnetic resonance images
(T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early …
(T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early …