The brain presents age-related structural and functional changes in the human life, with
different extends between subjects and groups. Brain age prediction can be used to …

The brain age is one of important biomarkers for identifying neurodegenerative diseases.
Most existing works construct prediction models based on brain MRIs by deep learning …

Convolutional neural networks (CNNs) have been used widely to predict biological brain
age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on …

With the use of neuroimaging data, deep learning holds enormous potential for precise
disease prediction, however, the size of the training dataset and the amount of memory …

In the process of human aging, significant age-related changes occur in brain tissue. To
assist individuals in assessing the degree of brain aging, screening for disease risks, and …

Predicting individual chronological age based on neuroimaging data is very promising and
important for understanding the trajectory of normal brain development. In this work, we …

The clinical applications of brain age prediction have expanded, particularly in anticipating
the onset and prognosis of various neurodegenerative diseases. In the current study, we …

Introduction In the last few years, several models trying to calculate the biological brain age
have been proposed based on structural magnetic resonance imaging scans (T1-weighted …

Precise prediction on brain age is urgently needed by many biomedical areas including
mental rehabilitation prognosis as well as various medicine or treatment trials. People …

Age-associated diseases rise as life expectancy increases. The brain presents age-related
structural changes across life, with different extends between subjects and groups. During …