3. Conclusions Two main conclusions can be drawn based on the examples in this
commentary: I) The method proposed by Behesti et al. provides age-bias correction that is …

Background: Chronic pain is a major issue affecting more than 50% of the older population
and up to 80% of nursing homes residents. Research on pain in the elderly focuses mainly …

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

Brain age prediction using machine‐learning techniques has recently attracted growing
attention, as it has the potential to serve as a biomarker for characterizing the typical brain …

Convolutional neural networks (CNN) can accurately predict chronological age in healthy
individuals from structural MRI brain scans. Potentially, these models could be applied …

Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently
develop Alzheimer's disease (AD) dementia. This impairment may be leveraged to aid …

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 …

Background Brain age is a biomarker that predicts chronological age using neuroimaging
features. Deviations of this predicted age from chronological age is considered a sign of age …

Objective During the natural course of multiple sclerosis (MS), the brain is exposed to aging
as well as disease effects. Brain aging can be modeled statistically; the so‐called “brain …

Sex differences influence brain morphology and physiology during both development and
aging. Here we apply a machine learning algorithm to a multiparametric brain PET imaging …