Marked changes occur in the brain during people's lives, and individual rates of aging have revealed pronounced differences, giving rise to subject-specific brainprints that are the …
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 …
S Guan, R Jiang, C Meng, B Biswal - GeroScience, 2024 - Springer
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant …
From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's …
The brain‐age‐gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine‐learning models to neuroimaging data and is …
Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the …
C Ran, Y Yang, C Ye, H Lv, T Ma - Human brain mapping, 2022 - Wiley Online Library
Neuroimaging‐driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) …
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, eg in health care, when …
To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI …