Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?
With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …
placing a growing burden on individuals and the whole society. However, individual rates of …
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 …
[HTML][HTML] Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors
JH Cole - Neurobiology of aging, 2020 - Elsevier
The brain-age paradigm is proving increasingly useful for exploring aging-related disease
and can predict important future health outcomes. Most brain-age research uses structural …
and can predict important future health outcomes. Most brain-age research uses structural …
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 …
Training confounder-free deep learning models for medical applications
The presence of confounding effects (or biases) is one of the most critical challenges in
using deep learning to advance discovery in medical imaging studies. Confounders affect …
using deep learning to advance discovery in medical imaging studies. Confounders affect …
ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries
This review summarizes the last decade of work by the ENIGMA (E nhancing N euro I
maging G enetics through M eta A nalysis) Consortium, a global alliance of over 1400 …
maging G enetics through M eta A nalysis) Consortium, a global alliance of over 1400 …
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 …
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 …
Burnout, career satisfaction, and well-being among US neurologists in 2016
Objective: To study prevalence of and factors that contribute to burnout, career satisfaction,
and well-being in US neurologists. Methods: A total of 4,127 US American Academy of …
and well-being in US neurologists. Methods: A total of 4,127 US American Academy of …
Brain-age prediction: A systematic comparison of machine learning workflows
S More, G Antonopoulos, F Hoffstaedter, J Caspers… - NeuroImage, 2023 - Elsevier
The difference between age predicted using anatomical brain scans and chronological age,
ie, the brain-age delta, provides a proxy for atypical aging. Various data representations and …
ie, the brain-age delta, provides a proxy for atypical aging. Various data representations and …