With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …

The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …

The brain-age paradigm is proving increasingly useful for exploring aging-related disease
and can predict important future health outcomes. Most brain-age research uses structural …

As our brains age, we tend to experience cognitive decline and are at greater risk of
neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases …

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy,
aging-related diseases, and mortality. We examined potential advanced brain aging in adult …

Cognitive impairment is a core symptom domain of schizophrenia. The effect of
antipsychotics, the cornerstone of treatment in schizophrenia, on this domain is not fully …

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 …

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 …

Convolutional neural networks typically consist of many convolutional layers followed by
one or more fully connected layers. While convolutional layers map between high-order …

The structure and integrity of the ageing brain is interchangeably linked to physical health,
and cardiometabolic risk factors (CMRs) are associated with dementia and other brain …