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 changes as we age and these changes are associated with functional
deterioration and neurodegenerative disease. It is vital that we better understand individual …

In mice, periodic cycles of a fasting mimicking diet (FMD) protect normal cells while killing
damaged cells including cancer and autoimmune cells, reduce inflammation, promote multi …

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 …

The relationship between obesity and cognitive impairment is important given the globally
ageing population in whom cognitive decline and neurodegenerative disorders will carry …

Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …

An individual's brainAGE is the difference between chronological age and age predicted
from machine-learning models of brain-imaging data. BrainAGE has been proposed as a …

Ageing is commonly associated with changes to segregation and integration of functional
brain networks, but, in isolation, current network-based approaches struggle to elucidate …

Conditions and comorbidities of obesity mirror those of ageing and age‐related diseases.
Obesity and ageing share a similar spectrum of phenotypes such as compromised genomic …