Deep learning (DL) methods have been increasingly applied to neuroimaging data to
identify patients with psychiatric and neurological disorders. This review provides an …

Recently, deep learning has unlocked unprecedented success in various domains,
especially using images, text, and speech. However, deep learning is only beneficial if the …

Abstract Systems medicine (SM) has emerged as a powerful tool for studying the human
body at the systems level with the aim of improving our understanding, prevention and …

Focusing on biomarker identification and using biomarkers individually or in clusters to
define biological subgroups in psychiatry requires a re-orientation from behavioral …

Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal
brain imaging data has enormous potential for neuroscientific and clinical research by …

Prediction of cognitive ability latent factors such as general intelligence from neuroimaging
has elucidated questions pertaining to their neural origins. However, predicting general …

Brain age, an estimated biological age from anatomical and/or functional brain imaging
data, and its deviation from the chronological age (brain age gap) have shown the potential …

As we age, our brain structure changes and our cognitive capabilities decline. Although
brain aging is universal, rates of brain aging differ markedly, which can be associated with …

Few-shot learning addresses problems for which a limited number of training examples are
available. So far, the field has been mostly driven by applications in computer vision. Here …

Deep neural networks (DNNs) are being increasingly used to make predictions from
functional magnetic resonance imaging (fMRI) data. However, they are widely seen as …