Summary: New research highlights how the brain’s reward-learning system can guide personalized treatments for depression. By studying two brain signals, expected value and prediction error, researchers identified markers that predict recovery potential and tailored responses to rewards and setbacks.
This approach goes beyond symptom management, targeting the brain processes driving specific depression symptoms like anhedonia. The findings pave the way for brain-based therapies that align with each individual’s unique learning patterns, offering more precise and effective mental health care.
Key Facts:
- Key Brain Signals: Expected value and prediction error predict recovery potential in depression.
- Personalized Therapy: Tailored approaches address specific symptoms like anhedonia.
- Future Impact: Brain-based models could transform depression care into a precise, individualized approach.
Source: Virginia Tech
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.
About this depression and neuroscience research news
Author: Leigh Anne Kelley
Source: Virginia Tech
Contact: Leigh Anne Kelley – Virginia Tech
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Reinforcement learning processes as forecasters of depression remission” by Pearl Chiu et al. Journal of Affective Disorders
Abstract
Reinforcement learning processes as forecasters of depression remission
Background
Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness.
Methods
We applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms.
Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit.
Results
Both nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status.
Limitations
Our sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling).
Conclusions
Results support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.
