The research, led by Diego A. Pizzagalli at the University of California, Irvine and colleagues at McLean Hospital, part of the Mass General Brigham system, is among the first to prospectively test a biomarker-guided strategy for antidepressant selection. The team focused on sertraline (Zoloft) and bupropion (Wellbutrin), medications that operate through fundamentally different neurochemical pathways. Sertraline blocks the reuptake of serotonin, prolonging its activity in synapses, while bupropion primarily affects norepinephrine and dopamine, neurotransmitters tied to motivation and reward. Because depression is increasingly understood not as a single disease but as a collection of distinct biological subtypes, the researchers hypothesized that measurable biological and behavioral signatures could reveal which drug would be more effective for a given individual.

To build predictive models, the scientists tapped data from the EMBARC study, a large national trial that collected functional magnetic resonance imaging (fMRI), cognitive assessments, and detailed clinical histories from hundreds of depressed patients before they started medication. The algorithms they constructed combined multiple features: resting-state connectivity in brain networks linked to emotion regulation and cognitive control, performance on tasks measuring reward sensitivity and executive function, depression severity scores, personality dimensions, and even employment status. These computationally intensive models identified patterns that distinguished likely responders to each drug from non-responders.

In a subsequent independent clinical trial, 48 adults with major depressive disorder underwent the same battery of tests and were then assigned to receive either sertraline or bupropion based on those algorithms. Notably, the investigators included a deliberate mismatch group—patients who received the drug that was not predicted to be optimal—to test whether matching mattered. Though the study did not have enough statistical power to detect a significant difference between the matched and mismatched arms, a much larger signal emerged when researchers looked at overall biomarker favorability. Patients whose profiles showed positive biological signatures for either or both medications had markedly better outcomes. The response rate reached 71.4 percent in those with dual favorable biomarkers, compared with 42.8 percent in those with none—a 67 percent relative improvement.

“Depression treatment still relies far too heavily on trial and error,” said Pizzagalli, founding director of the Noel Drury, M.D. Institute for Translational Depression Discoveries at UC Irvine. “Patients often spend months cycling through medications before finding one that works, while symptoms worsen and suicide risk can increase. Our findings suggest we may be able to move psychiatry closer to precision medicine, where objective biological and behavioral data help guide treatment decisions from the outset.” The fact that the algorithms captured something real about the underlying biology, even without perfect drug matching, indicates that measurable neural and cognitive markers can identify individuals who are overall more likely to get better with these standard treatments.

The technical backbone of the study relies on resting-state fMRI, a technique that maps correlations in spontaneous brain activity across regions. The models highlighted the importance of connectivity within the default mode network, a system implicated in self-referential thought and rumination, and between the frontoparietal control network and subcortical reward circuits. Deficits in reward processing, measured by a task in which participants had to learn to choose stimuli that were more frequently rewarded, turned out to be particularly predictive of bupropion response—a plausible finding given the drug’s dopaminergic effects. Cognitive control metrics, which tap the ability to inhibit automatic responses, helped forecast sertraline outcomes, aligning with serotonin’s role in modulating prefrontal executive functions.

Despite the excitement, the researchers caution that the technology is not yet ready for the clinic. The final sample size was small, and the predictive tools depend on expensive and time-consuming MRI scans that are currently impractical for most psychiatric practices. Moreover, the algorithms need to be validated in larger, more diverse populations before they can be trusted to inform real-world decisions. Still, the work represents a critical proof of concept for the emerging field of precision psychiatry, which aspires to emulate oncology’s successes in using molecular biomarkers to guide therapy.

If refined and made more accessible, biomarker-guided approaches could fundamentally alter the treatment landscape. Beyond selecting between sertraline and bupropion, future iterations might help clinicians rapidly identify the roughly half of patients unlikely to respond to any first-line antidepressant, steering them more swiftly toward psychotherapy, repetitive transcranial magnetic stimulation, or ketamine-based interventions. Such a shift would not only spare patients months of futile suffering but also reduce the enormous economic and social costs of inadequately treated depression, which affects more than 280 million people worldwide.

“This study is an early but important proof of concept,” Pizzagalli emphasized. “It lays the groundwork for larger studies that could ultimately transform how we treat depression. These are the types of studies that we will prioritize within the recently launched Noel Drury, M.D. Institute for Translational Depression Discoveries at UC Irvine.” The research, supported by the National Institute of Mental Health and the Wellcome Leap program, heralds a future in which picking an antidepressant may become less of a blind leap and more of a measured step guided by the brain itself.

Subject of Research: Biomarker-guided antidepressant treatment selection for major depressive disorder
Article Title: A precision medicine trial of bupropion and sertraline for major depressive disorder using a biomarker-guided sequential multiple-assignment design
News Publication Date: July 6, 2026
Web References: https://www.nature.com/articles/s44220-026-00671-z; https://depressioninstitute.uci.edu/
References: doi:10.1038/s44220-026-00671-z
Image Credits: Not provided
Keywords: precision psychiatry, major depressive disorder, biomarkers, antidepressants, sertraline, bupropion, functional MRI, machine learning, EMBARC, treatment selection