Brain Scans May Soon Predict Best Antidepressant for You

Published in JAMA Network Open, the study presents promising advances in predicting how patients with major depressive disorder (MDD) will respond to antidepressant medications. By combining brain imaging with clinical data, researchers found that patterns of brain connectivity, particularly within the dorsal anterior cingulate cortex, significantly improved the ability to predict treatment outcomes across two large, independent clinical trials.

“In spite of the availability of several antidepressant treatments, including medications and psychotherapy, many individuals with depression have difficulties finding the treatment that works best for them,” said Diego Pizzagalli, PhD, director of the Noel Drury, M.D. Institute for Translational Depression Discoveries at UC Irvine and Distinguished Professor at the Charlie Dunlop School of Biological Sciences and the School of Medicine. “As a result, for many, treatment follows a trial-and-error approach. Discovering brain-based markers predicting positive antidepressant response promises to allow a more personalized treatment and thereby speed up reduction of symptoms.”

The research team used machine learning models trained on clinical and neuroimaging data from over 350 participants enrolled in two international studies: EMBARC (U.S.) and CANBIND-1 (Canada). They tested whether their algorithms could accurately predict who would respond to commonly prescribed antidepressants such as 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.

“We identified a brain connectivity marker that was predictive of response to common antidepressants across two large-scale clinical trials in the U.S. and Canada,” explained Peter Zhukovsky, a former postdoctoral fellow in Dr. Pizzagalli’s laboratory and now Scientist in the Brain Health Imaging Centre at the Centre for Addiction and Mental Health (CAMH) and first author of the study. “The predictive performance of our algorithm was improved by the addition of the brain connectivity feature to clinical and demographic markers, reaching moderate levels. Our findings are promising for the search for biomarkers predicting depression response. We hope these efforts will help connect patients with treatments that are most likely to work for them.”

The study also tackled the often-overlooked challenge of generalizability — whether a prediction model developed in one trial will hold up in a completely separate population. That’s where this research stands out. Models trained on one trial performed surprisingly well when tested on another, highlighting the potential for broader real-world use.

“Data harmonization and building a large-scale database with different treatments is challenging,” noted Zhukovsky. “However, we’re hopeful that cross-trial analyses such as the one we conducted in this project will advance precision medicine goals.”

The study’s implications are far-reaching. By developing biomarkers that are not limited to one treatment setting or population, researchers are laying the groundwork for clinical tools that could eventually match patients with effective treatments earlier, potentially reducing suffering and speeding recovery.

“We investigated biomarkers predicting antidepressant treatment response,” Zhukovsky added. “However, many other options are available for treating depression and if we can identify markers for specific treatments, then the resulting decision support tools could be tested in biomarker-guided clinical studies.”

As mental health disorders continue to rise globally, the need for faster, data-driven treatment approaches is more urgent than ever. The team’s findings underscore the promise of brain-based diagnostics to transform how depression is treated. But they also stress that more research is needed — larger trials, new treatment comparisons, and real-world implementation studies — to bring these insights from the lab to the clinic. This line of work will be one of the key priorities within the recently launched Noel Drury, M.D. Institute for Translational Depression Discoveries at UC Irvine.

4155/v