In a study in the Journal of Affective Disorders, Fralin Biomedical Research Institute scientists Pearl Chiu and Brooks Casas investigate how brain signals involved in reward learning might help personalize depression treatment.
A brain signal that lights up when we anticipate rewards may hold the secret to helping people overcome depression, and Virginia Tech researchers are working to unlock its potential.
Professors Pearl Chiu and Brooks Casas of the Fralin Biomedical Research Institute at VTC are pioneering a personalized approach to depression treatment by exploring how our brains process rewards and setbacks.
Their study, which published in January in the Journal of Affective Disorders , examines two brain signals — prediction error and expected value — that may predict whether someone with depression is likely to see their symptoms improve.
Unlocking the brain's reward system
Major depression affects over 21 million Americans annually, according to the Centers for Disease Control and Prevention, and remains a leading cause of disability worldwide. Yet current treatments often fall short, leaving many without lasting relief.
"Major depression isn't one-size-fits-all," Chiu said. "People with depression learn and respond to rewards and setbacks differently, often in ways that align with specific symptoms."
Using computational models, the researchers studied how the brain's reward-learning system functions in those with depression, especially among individuals experiencing anhedonia, the inability to feel pleasure. By analyzing dopamine-linked responses, they identified unique brain activity patterns that could help predict who is likely to recover.
Their responses reveal the brain's capacity to learn from outcomes, Chiu said, and could form the basis for a new kind of therapy using tailored learning processes to guide the brain's responses to different outcomes.
Researchers identify key markers for recovery
The study identified two key brain signals — prediction error and expected value — as essential indicators of recovery potential in depression. Expected value, which reflects the brain's anticipation of rewards and guides decision-making, emerged as a consistent predictor of remission across treatment types. Prediction error, which highlights gaps between expected and actual outcomes to help individuals adjust their behavior, offered additional insights.
Together, prediction error and expected value provided a richer understanding of how unique learning patterns influence mental health outcomes, paving the way for tailored, symptom-specific therapies.
"This finding underscores the power of the brain's reward system in forecasting recovery," Casas said. "By observing how each person responds to rewards and setbacks, we can open new pathways for designing treatments that match individual learning patterns."
"This brings us closer to truly personalized mental health care," noted Vansh Bansal, first author of the study and a graduate student with Chiu and Casas.
Bridging brain science and therapy
The researchers are putting their insights into practice in new ways. Earlier this year, Chiu and Casas published work in Clinical Psychological Science that explored how reinforcement-learning questions could guide behavior change. Now, they are taking this approach a step further by testing specific questions designed to shift how people with depression respond to rewards and setbacks.
"We're exploring questions like, 'What did you expect to happen?' to reshape how the brain learns from experiences," Chiu said.
This approach aims to go beyond symptom management, targeting the brain processes that drive specific symptoms of depression. By aligning therapy with each person's unique brain responses, this strategy could lead to more targeted, symptom-specific interventions that deliver lasting results.
This research represents an advance in bridging brain science and therapy, moving toward more personalized, effective treatment methods. By understanding how the brain's reward system functions, the researchers are developing strategies that could reshape depression care by addressing its root causes rather than just symptoms.
"Our goal is to create a treatment that bridges neuroscience and behavioral therapies," Chiu said. "If someone's brain responds less strongly to rewards, we might use behavioral activation to amplify their recovery." This method aligns treatment with each person's neural responses, setting the stage for more customized, symptom-specific interventions that reach beyond traditional approaches.
A future of personalized depression treatment
Looking ahead, the team envisions the use of brain-based models to transform depression treatment into a precise, individualized approach. Imagine a patient completing an assessment and, based on the results, receiving interventions tailored to their unique learning processes. For some, this could involve exercises to counteract the inability to feel pleasure or strategies to strengthen positive responses.
"The true benefit is that this approach doesn't just treat symptoms on the surface," Chiu said. "It addresses the underlying learning mechanisms contributing to each person's unique experience of depression."
This model could enable therapists to offer precise, evidence-based techniques to retrain the brain's responses and accelerate recovery.
"We're moving toward a future where mental health care is as unique as each person's mind," Casas said. "By aligning treatments with individual learning styles, we can go beyond symptom management and foster truly lasting recovery and resilience."
In addition to the Fralin Biomedical Research Institute, Chiu and Casas are members of the Department of Psychology in Virginia Tech's College of Science .
The study was a collaboration involving experts from multiple institutions, including Vansh Bansal, Jonathan Lisinski, Dong-Youl Kim, Shivani Goyal, John Wang, Jacob Lee, and Stephen LaConte, all affiliated with Virginia Tech. Katherine McCurry from the University of Michigan and Vanessa Brown from Emory University also contributed to the study.
