We propose a new framework for estimating neuroimaging-derived “brain-age” at a local
level within the brain, using deep learning. The local approach, contrary to existing global …

Many machine learning tasks in neuroimaging aim at modeling complex relationships
between a brain's morphology as seen in structural MR images and clinical scores and …

Problem: Chronological aging in later life is associated with brain degeneration processes
and increased risk for disease such as stroke and dementia. With a worldwide tendency of …

Though aging is ubiquitous, the rate at which age-associated biological changes in the brain
occur differs substantially between individuals. Building on this, the so-called brain-age …

Age is an important variable to describe the expected brain's anatomy status across the
normal aging trajectory. The deviation from that normative aging trajectory may provide …

Predicting brain aging can help in the early detection and prognosis of neurodegenerative
diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance …

Brain aging, and more specifically the difference between the chronological and the
biological age of a person, may be a promising biomarker for identifying neurodegenerative …

Global brain age estimation has been used as an effective biomarker to study the correlation
between brain aging and neurological disorders. However, it fails to provide spatial …

The lack of explainability of deep learning models limits the adoption of such models in
clinical practice. Prototype-based models can provide inherent explainable predictions, but …

Brain age estimation involves predicting an individual's biological age from their brain
images. This process offers valuable insights into the aging process and the progression of …