New Brain Signal Analysis Distinguishes Parkinson’s Disease from Essential Tremor
Researchers have identified a potential new biomarker for differentiating between Parkinson’s disease and essential tremor,two neurological conditions often difficult to distinguish. The breakthrough stems from analyzing how the brain processes unexpected outcomes in social interactions, and linking those processes to serotonin activity.
The study, published in Nature Communications, leveraged a computational model to analyze brain responses of individuals with either Parkinson’s or essential tremor. According to the Parkinson’s Foundation, Parkinson’s affects roughly 1 million Americans and over 10 million people worldwide. Essential tremor is even more prevalent,impacting an estimated 7 million Americans,according to Columbia University research.
The key finding revolves around “prediction errors” – the discrepancies between what a person anticipates and what actually happens. Researchers discovered that the strength of the brain’s response to these mismatches, specifically changes in serotonin activity, served as a strong indicator of which condition a patient had.
“What they added was a computational model of what the subjects expected would happen,” explained researcher Howe. “When we reframed the data that way, we were able to reveal a difference in how the brain responded in these two patient groups.”
This research builds upon decades of work in dopamine signaling, utilizing parameters refined by the lab of researcher Montague. The team extracted dopamine and serotonin signals using these established models.The initial data was collected by Kenneth Kishida while at the Fralin Biomedical Research Institute, and the collaboration with Wake Forest University proved crucial to the study’s success.
“It’s exciting to see that effort applied in a way that might help diagnose or stratify real clinical populations,” Montague stated.
Dan Bang, an associate professor involved in the study, emphasized the significance of linking internal beliefs to measurable brain chemistry. “It’s very powerful to link moment-to-moment changes in internal beliefs-here what a person expects from others-to measurable chemical signals in the brain. This opens a new window into how deeply human cognitive processes, like social evaluation, are shaped by disease.”
The project’s success was also attributed to iterative model refinement and a collaborative,cross-disciplinary approach. “These models improve over time as they’re trained on more data,” noted co-author Seth batten. “But just as vital was the collaborative approach-bringing in new people with different expertise allowed us to see patterns we hadn’t recognized before.”
Researchers from Virginia Tech and wake Forest University School of Medicine contributed to the study, which was funded by the National Institutes of Health (including multiple institutes focused on diabetes, mental health, translational sciences, drug abuse, and neurological disorders), the Lundbeck Foundation, and the Red Gates Foundation.
The team views this as a foundational step, with Howe concluding, “This study tells a compelling story, but the story doesn’t end here.” Further research is planned to build upon these findings and explore potential diagnostic and therapeutic applications.
