Researchers employed computational models to explore how the brain learns about rewards in individuals with depression, focusing particularly on those who struggle with anhedonia, the inability to experience pleasure. Their findings, which investigate dopamine-related brain responses, reveal distinct patterns of brain activity that may help indicate how likely someone is to recover.
In an article published in the Journal of Affective Disorders, scientists Pearl Chiu and Brooks Casas from the Fralin Biomedical Research Institute explore how insights from brain activity related to reward learning can support personalized treatment strategies for depression.
A brain signal that activates when anticipating rewards could be crucial in helping people overcome depression. Researchers at Virginia Tech are striving to harness this potential.
Professors Pearl Chiu and Brooks Casas, leading the research at Virginia Tech’s Fralin Biomedical Research Institute, are investigating how the brain processes rewards and setbacks to develop personalized treatments for depression.
In their January study published in the Journal of Affective Disorders, the researchers looked at two key signals in the brain—prediction error and expected value—which may help gauge if a person with depression will see an improvement in their symptoms.
Investigating the brain’s reward mechanisms
According to the Centers for Disease Control and Prevention, major depression affects over 21 million Americans each year, making it a leading cause of disability worldwide. Unfortunately, many current treatments fail to deliver long-term relief.
“Major depression is not identical for everyone,” Chiu stated. “Individuals with depression react differently to rewards and challenges, often related to their unique symptoms.”
By leveraging computational models, the researchers studied the reward-learning process in people with depression, especially those dealing with anhedonia. They analyzed dopamine-related activities and identified specific brain patterns that could predict recovery likelihood.
Chiu noted that these brain responses reflect the capacity to learn from experiences, suggesting the possibility of new treatment methods that personalize learning to affect how the brain responds to different outcomes.
Identifying key indicators of recovery
The study highlighted two essential signals—prediction error and expected value—as critical indicators for assessing recovery chances in depression. The expected value reflects how the brain anticipates rewards and assists in decision-making, consistently indicating potential remission across various treatments. Prediction error, on the other hand, represents the gap between expectations and reality, providing insights for behavior modification.
Together, these signals offered a holistic view of how personalized learning strategies could influence mental health outcomes, paving the way for tailored, symptom-focused therapies.
“This discovery emphasizes the crucial role of the reward system in forecasting recovery,” Casas remarked. “By tracking individual responses to rewards and challenges, we might uncover new treatment strategies aligned with specific personal learning behaviors.”
“We’re approaching genuine personalized mental health care,” added Vansh Bansal, the study’s lead author and graduate student collaborating with Chiu and Casas.
Bridging neuroscience and therapy
The researchers are actively applying their findings in innovative ways. Earlier this year, Chiu and Casas published results in *Clinical Psychological Science*, illustrating how reinforcement-learning studies could drive behavioral changes. They are now furthering this methodology by experimenting with targeted questions to change how individuals with depression respond to rewards and challenges.
“We’re exploring questions such as, ‘What were you expecting to happen?’ to reshape the way the brain interprets experiences,” Chiu explained.
This approach aims to go beyond simply managing symptoms, addressing the core brain functions that contribute to specific depressive symptoms. By aligning therapeutic practices with each person’s neural responses, the research seeks to create targeted interventions that provide longstanding benefits.
This work signifies progress toward integrating brain science with therapeutic applications, advancing towards more personalized and effective treatment choices. By understanding the brain’s reward system, the researchers are developing strategies that tackle the root causes of depression rather than just its symptoms.
“Our goal is to design therapies that connect neuroscience with behavioral methods,” Chiu said. “If a person’s brain shows a weak response to rewards, we can use behavioral activation to enhance their recovery.” This tactic customizes treatments to fit each individual’s neural reactions, enabling more focused and symptom-specific interventions than traditional methods.
The future of personalized depression treatment
Looking ahead, the researchers aim to utilize brain-based models to refine depression treatment into a highly personalized approach. Imagine a patient undergoing an evaluation, after which they would receive targeted interventions that correspond to their unique learning styles. For some, this could mean addressing anhedonia, while for others, it could enhance responses to positive feedback.
“The key benefit is that this approach digs deeper than mere symptoms,” Chiu stated. “It aims at the underlying learning processes that influence each person’s experience of depression.”
This framework could enable therapists to apply precise, evidence-based techniques that retrain the brain’s responses, leading to faster recovery.
“We’re moving towards a reality where mental health care is as individual as every mind,” Casas remarked. “By matching treatments to personal learning styles, we can move beyond basic symptom management to truly support lasting recovery and resilience.”
In addition to their roles at the Fralin Biomedical Research Institute, Chiu and Casas are also connected with Virginia Tech’s Department of Psychology in the College of Science.
This study was a collaborative venture, involving experts from various institutions, including Vansh Bansal, Jonathan Lisinski, Dong-Youl Kim, Shivani Goyal, John Wang, Jacob Lee, and Stephen LaConte, all associated with Virginia Tech, along with contributions from Katherine McCurry of the University of Michigan and Vanessa Brown from Emory University.
