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 …

In neuroimaging, the difference between predicted brain age and chronological age, known
as brain age delta, has shown its potential as a biomarker related to various pathological …

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 …

Brain aging is a complex process, while its mechanism remains unknown. Due to the
accuracy and high reliability of brain age as a phenotype, which can be used to track the …

predicting brain age using Magnetic Resonant Imaging (MRI) and its difference with
chronological age is useful for detecting Alzheimer's disease in the early stages. For having …

The brain age, estimated from the brain MRI data, is found to be a promising biomarker for
human brain development and neuroanatomical aging processes. A well-performed brain …

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 …

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 …

Predicting brain age is needed urgently in the biomedical domain. People began to realize
that contrasting physical (real) age and predicted brain age can help to highlight brain …

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 …