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 …

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 …

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 …

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

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting
that brain structure is often 'older'than expected at a given chronological age. Whether …

Background Methamphetamine use is surging globally. It has been linked to premature
stroke, Parkinsonism, and dementia, suggesting that it may accelerate brain aging. Methods …

Brain anatomical age is an effective feature to assess the status of the brain, such as atypical
development and aging. Although some deep learning models have been developed for …

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 …