No tools are currently available to predict whether a patient suffering from major depressive
disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic …

Objective: Response to antidepressant treatment in major depressive disorder varies
substantially between individuals, which lengthens the process of finding effective treatment …

Background In order to reduce unsuccessful treatment trials for depression, neuroimaging
and genetic information can be considered as biomarkers. Together with machine‐learning …

Introduction: The prediction of antidepressant treatment response may improve outcome.
Functional magnetic resonance imaging (fMRI) of emotion processing in major depressive …

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-
standing clinical challenge has prompted an increased focus on predictive models of …

Introduction The heterogeneity of symptoms and complex etiology of depression pose a
significant challenge to the personalization of treatment. Meanwhile, the current application …

Several behavioral and neuroimaging markers could be used to predict eventual
antidepressant medication (ADM) outcomes in patients with major depressive disorder …

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 …

Objective Machine learning (ML) has been widely used to detect and evaluate major
depressive disorder (MDD) using neuroimaging data, ie, resting-state functional magnetic …

Background Growing evidence documents the potential of machine learning for developing
brain based diagnostic methods for major depressive disorder (MDD). As symptom severity …