Given the wide success of convolutional neural networks (CNNs) applied to natural images,
researchers have begun to apply them to neuroimaging data. To date, however, exploration …

Predicted brain age could be used to measure individual brain status over development and
degeneration, which could also indicate the potential risk of age-related brain disorders …

Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently
been used for brain age prediction and have achieved outstanding performance …

Deep neural networks have shown promise in predicting the chronological age of a healthy
brain using T1-weighted magnetic resonance images (T1 MRIs). This predicted brain age …

Brain age prediction is closely related to brain health, which is an important index to
estimate the state of brain health. In this paper, we use the 3D ResNet34 model to predict …

We propose a multi-hop graph attention module (MGA) that addresses the limitation of CNN
in capturing non-local connections of features for predicting brain age. MGA converts feature …

Brain age prediction has been shown to be clinically relevant, with the errors in the
prediction associated with various psychiatric and neurological conditions. While the …

Chronological age of healthy brain is able to be predicted using deep neural networks from
T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could …

In the pursuit of studying brain aging, numerous models to predict brain age from T1-
weighted MRI have been developed. Recently, many of these models take advantage of …

Brain age prediction using neuroimaging data has shown great potential as an indicator of
overall brain health and successful aging, as well as a disease biomarker. Deep learning …