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 …
Predicting age using neuroimaging: innovative brain ageing biomarkers
The brain changes as we age and these changes are associated with functional
deterioration and neurodegenerative disease. It is vital that we better understand individual …
deterioration and neurodegenerative disease. It is vital that we better understand individual …
Machine learning applications in epilepsy
B Abbasi, DM Goldenholz - Epilepsia, 2019 - Wiley Online Library
Abstract Machine learning leverages statistical and computer science principles to develop
algorithms capable of improving performance through interpretation of data rather than …
algorithms capable of improving performance through interpretation of data rather than …
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 …
Brain age and other bodily 'ages': implications for neuropsychiatry
As our brains age, we tend to experience cognitive decline and are at greater risk of
neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases …
neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases …
The burden of epilepsy and unmet need in people with focal seizures
P Ioannou, DL Foster, JW Sander, S Dupont… - Brain and …, 2022 - Wiley Online Library
Background Epilepsy is one of the most common neurological conditions worldwide. As a
chronic condition, epilepsy imposes a significant burden on people with epilepsy and …
chronic condition, epilepsy imposes a significant burden on people with epilepsy and …
Mind the gap: Performance metric evaluation in brain‐age prediction
Estimating age based on neuroimaging‐derived data has become a popular approach to
developing markers for brain integrity and health. While a variety of machine‐learning …
developing markers for brain integrity and health. While a variety of machine‐learning …
[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 …
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 …