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 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 …

Background “Brain-predicted age” estimates biological age from complex, nonlinear
features in neuroimaging scans. The brain age gap (BAG) between predicted and …

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 …

Neuroimaging research has uncovered a multitude of neural abnormalities associated with
psychopathology, but few prediction-based studies have been conducted during …

One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain
Age, or a predicted value based on machine-learning models built to predict chronological …

Graph convolutional networks (GCN) leverage topology-driven graph convolutional
operations to combine information across the graph for inference tasks. In our recent work …

Since the introduction of the BrainAGE method, novel machine learning methods for brain
age prediction have continued to emerge. The idea of estimating the chronological age from …

Puberty is linked to mental health problems during adolescence, and in particular, the timing
of puberty is thought to be an important risk factor. This study developed a new measure of …

Brain age algorithms using data science and machine learning techniques show promise as
biomarkers for neurodegenerative disorders and aging. However, head motion during MRI …