Regional neuroanatomic effects on brain age inferred using magnetic resonance imaging and ridge regression
The biological age of the brain differs from its chronological age (CA) and can be used as
biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age …
biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age …
Patch-based brain age estimation from MR images
Brain age estimation from Magnetic Resonance Images (MRI) derives the difference
between a subject's biological brain age and their chronological age. This is a potential …
between a subject's biological brain age and their chronological age. This is a potential …
Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
AC Roibu, S Adaszewski, T Schindler… - 2023 10th IEEE …, 2023 - ieeexplore.ieee.org
Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked
by numerous structural and functional changes. These can cause discrepancies between …
by numerous structural and functional changes. These can cause discrepancies between …
[HTML][HTML] The effect of head motion on brain age prediction using deep convolutional neural networks
P Vakli, B Weiss, D Rozmann, G Erőss, Á Nárai… - NeuroImage, 2024 - Elsevier
Deep learning can be used effectively to predict participants' age from brain magnetic
resonance imaging (MRI) data, and a growing body of evidence suggests that the difference …
resonance imaging (MRI) data, and a growing body of evidence suggests that the difference …
Patch‐wise brain age longitudinal reliability
We recently introduced a patch‐wise technique to estimate brain age from anatomical T1‐
weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its …
weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its …
Optimising a simple fully convolutional network for accurate brain age prediction in the PAC 2019 challenge
Brain age prediction from brain MRI scans not only helps improve brain ageing modelling
generally, but also provides benchmarks for predictive analysis methods. Brain-age delta …
generally, but also provides benchmarks for predictive analysis methods. Brain-age delta …
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 …
Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias
Abstract Machine learning has been increasingly applied to neuroimaging data to predict
age, deriving a personalized biomarker with potential clinical applications. The scientific and …
age, deriving a personalized biomarker with potential clinical applications. The scientific and …
Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network
I Hwang, EK Yeon, JY Lee, RE Yoo, KM Kang… - Neurobiology of …, 2021 - Elsevier
Our study investigated the feasibility and clinical relevance of brain age prediction using
axial T2-weighted images (T2-WIs) with a deep convolutional neural network (CNN) …
axial T2-weighted images (T2-WIs) with a deep convolutional neural network (CNN) …
Examining the reliability of brain age algorithms under varying degrees of participant motion
JL Hanson, DJ Adkins, E Bacas, P Zhou - Brain Informatics, 2024 - Springer
Brain age algorithms using data science and machine learning techniques show promise as
biomarkers for neurodegenerative disorders and aging. However, head motion during MRI …
biomarkers for neurodegenerative disorders and aging. However, head motion during MRI …