Published in JAMA Network Open, the study reveals promising progress toward predicting how patients with major depressive disorder (MDD) will respond to antidepressant medications using brain imaging and clinical data. The research demonstrated that brain connectivity patterns — specifically in the dorsal anterior cingulate cortex — could significantly improve predictions of treatment response across two large, independent clinical trials.Using machine learning models trained on clinical and neuroimaging data from more than 350 participants in two international trials — EMBARC in the U.S. and CANBIND-1 in Canada — the researchers evaluated whether their algorithms could reliably predict who would respond to common antidepressants like sertraline and escitalopram. They found that adding a brain connectivity marker to traditional clinical data (such as age, sex and baseline depression severity) significantly improved prediction performance across both studies.

Study finds a 18.5% reduction in sick leave following LEZ implementation in Greater London compared to areas  in England without low emission zones.