Neurological and psychiatric diseases have high degrees of genetic and pathophysiological
heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have …

Deep learning algorithms have a huge influence on tackling research issues in the field of
medical image processing. It acts as a vital aid for the radiologists in producing accurate …

The discrepancy between chronological age and the apparent age of the brain based on
neuroimaging data—the brain age delta—has emerged as a reliable marker of brain health …

Federated learning (FL) enables distributed computation of machine learning models over
various disparate, remote data sources, without requiring to transfer any individual data to a …

Combining deep learning image analysis methods and large-scale imaging datasets offers
many opportunities to neuroscience imaging and epidemiology. However, despite these …

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory,
thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD …

Alzheimer's disease (AD) is a global health issue that predominantly affects older people. It
affects one's daily activities by modifying neural networks in the brain. AD is categorized by …

Highlights•Neural mechanisms underlying stress and resilience are discussed.•Genetic
factors may partially determine why only some individuals develop resilience.•Machine …

Background Retinoblastoma is the most common intraocular malignancy in childhood. With
the advanced management strategy, the globe salvage and overall survival have …

There is significant interest in pooling magnetic resonance image (MRI) data from multiple
datasets to enable mega-analysis. Harmonization is typically performed to reduce …