Aging biomarkers are a combination of biological parameters to (i) assess age-related
changes,(ii) track the physiological aging process, and (iii) predict the transition into a …

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to
age-related brain changes. The present study systematically reviewed brainAGE findings in …

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death
worldwide. Current COPD diagnosis (ie, spirometry) could be unreliable because the test …

The difference between the estimated brain age and the chronological age ('brain-PAD')
could become a clinical biomarker. However, most brain age models were developed for …

XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of
complex models. XAI is especially required in sensitive applications, eg in health care, when …

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

An individual's brain predicted age minus chronological age (brain-PAD) obtained from
MRIs could become a biomarker of disease in research studies. However, brain age reports …

Brain regions' rates of age-related volumetric change after traumatic brain injury (TBI) are
unknown. Here, we quantify these rates cross-sectionally in 113 persons with recent mild …

The Zingiberaceae family possess various phenolic compounds that have significant
systemic bioactivities in the brain, including in age-related neurodegenerative diseases …

Brain age is the estimate of biological age derived from neuroimaging datasets using
machine learning algorithms. Increasing brain age with respect to chronological age can …