Digital health data are multimodal and high-dimensional. A patient's health state can be
characterized by a multitude of signals including medical imaging, clinical variables …

Abstract Machine learning has enhanced the abilities of neuroscientists to interpret
information collected through EEG, fMRI, and MEG data. With these powerful techniques …

Importance Biological psychiatry aims to understand mental disorders in terms of altered
neurobiological pathways. However, for one of the most prevalent and disabling mental …

Recent years have marked a renaissance in efforts to increase research reproducibility in
psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid …

Background Establishing objective and quantitative neuroimaging biomarkers at individual
level can assist in early and accurate diagnosis of major depressive disorder (MDD) …

This paper discusses the promising areas of research into machine learning applications for
the prevention and correction of neurodegenerative and depressive disorders. These two …

Background Radiomics is the extraction of predefined mathematic features from medical
images for the prediction of variables of clinical interest. While some studies report …

Pregnancy and the postpartum period are characterized by an increased neuroplasticity in
the maternal brain. To explore the dynamics of postpartum changes in gray matter volume …

Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …