In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …

Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …

Background Despite many successes, the case-control approach is problematic in
biomedical science. It introduces an artificial symmetry whereby all clinical groups (eg …

Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

In the past years, mass univariate statistical analyses of neuroimaging data have been
complemented by the use of multivariate pattern analyses, especially based on machine …

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the
future, as opposed to making a diagnosis, which is concerned with the current state. During …

Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels,
including symptoms, disease course, and biological underpinnings. These form a …

Fully automated classification algorithms have been successfully applied to diagnose a wide
range of neurological and psychiatric diseases. They are sufficiently robust to handle data …

Depression is a complex clinical entity that can pose challenges for clinicians regarding both
accurate diagnosis and effective timely treatment. These challenges have prompted the …