Brain-age estimation has gained increased attention in the neuroscientific community owing
to its potential use as a biomarker of brain health. The difference between estimated and …

Brain aging is a complex, multifaceted process that can be challenging to model in ways that
are accurate and clinically useful. One of the most common approaches has been to apply …

Estimating age based on neuroimaging‐derived data has become a popular approach to
develo** markers for brain integrity and health. While a variety of machine‐learning …

The discrepancy between chronological age and the apparent age of the brain based on
neuroimaging data—the brain age delta—has emerged as a reliable marker of brain health …

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments,
age-related chronic disease, and premature mortality. We investigated evidence for …

Brain age is a widely used index for quantifying individuals' brain health as deviation from a
normative brain aging trajectory. Higher-than-expected brain age is thought partially to …

Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to
assess the biological age of the human brain. The difference between a person's …

Most deep learning models for temporal regression directly output the estimation based on
single input images, ignoring the relationships between different images. In this paper, we …

Abstract Machine learning has been increasingly applied to neuroimaging data to predict
age, deriving a personalized biomarker with potential clinical applications. The scientific and …

In computational neuroscience, there has been an increased interest in develo** machine
learning algorithms that leverage brain imaging data to provide estimates of" brain age" for …