BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …

Identifying biomarkers for major depression is of high importance for improving diagnosis
and treatment of this common and debilitating neuropsychiatric disorder, as the field seeks …

This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …

Background No previous study has comprehensively reviewed the application of machine
learning algorithms in mood disorders populations. Herein, we qualitatively and …

Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …

Informatics paradigms for brain and mental health research have seen significant advances
in recent years. These developments can largely be attributed to the emergence of new …

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex
(DLPFC) is an established therapy for refractory depression. However, treatment outcomes …

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 …

The brain works as an organised, network-like structure of functionally interconnected
regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of …

The extent to which working memory training improves performance on untrained tasks is
highly controversial. Here we address this controversy by testing the hypothesis that far …