Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
Machine learning for brain age prediction: Introduction to methods and clinical applications
The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …
data, including brain age prediction. In this state-of-the-art review, we provide an …
Mind the gap: Performance metric evaluation in brain‐age prediction
Estimating age based on neuroimaging‐derived data has become a popular approach to
develo** markers for brain integrity and health. While a variety of machine‐learning …
develo** markers for brain integrity and health. While a variety of machine‐learning …
[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 …
Factors associated with brain ageing-a systematic review
Background Brain age is a biomarker that predicts chronological age using neuroimaging
features. Deviations of this predicted age from chronological age is considered a sign of age …
features. Deviations of this predicted age from chronological age is considered a sign of age …
Longitudinal assessment of multiple sclerosis with the brain‐age paradigm
Objective During the natural course of multiple sclerosis (MS), the brain is exposed to aging
as well as disease effects. Brain aging can be modeled statistically; the so‐called “brain …
as well as disease effects. Brain aging can be modeled statistically; the so‐called “brain …
[HTML][HTML] Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study
Brain age is becoming a widely applied imaging-based biomarker of neural aging and
potential proxy for brain integrity and health. We estimated multimodal and modality-specific …
potential proxy for brain integrity and health. We estimated multimodal and modality-specific …
[HTML][HTML] Spatial transcriptomic clocks reveal cell proximity effects in brain ageing
ED Sun, OY Zhou, M Hauptschein, N Rappoport, L Xu… - Nature, 2024 - nature.com
Old age is associated with a decline in cognitive function and an increase in
neurodegenerative disease risk. Brain ageing is complex and is accompanied by many …
neurodegenerative disease risk. Brain ageing is complex and is accompanied by many …
Cardiometabolic risk factors associated with brain age and accelerated brain ageing
The structure and integrity of the ageing brain is interchangeably linked to physical health,
and cardiometabolic risk factors (CMRs) are associated with dementia and other brain …
and cardiometabolic risk factors (CMRs) are associated with dementia and other brain …
Brain age prediction: A comparison between machine learning models using brain morphometric data
Brain structural morphology varies over the aging trajectory, and the prediction of a person's
age using brain morphological features can help the detection of an abnormal aging …
age using brain morphological features can help the detection of an abnormal aging …