Researchers identify brain network with mapping technique

Parkinson's disease is the second most common neurodegenerative disease in the US. Those who suffer from Parkinson's disease most often experience tremors, slowness of movement (bradykinesia), rigidity, and impaired balance and coordination. Patients may have difficulty walking, talking or completing simple daily tasks. They may also experience depression and difficulty sleeping due to the disease. The current standard for diagnosis of Parkinson's disease relies on a skilled healthcare professional, usually an experienced neurologist, to determine through clinical examination that someone has it. There currently is no cure for Parkinson's disease, but medications can improve symptoms.

A team of researchers at the Feinstein Institute's Center for Neurosciences, led by David Eidelberg, MD, has developed a method to identify brain patterns that are abnormal or indicate disease using imaging techniques. To date, this approach has been used successfully to identify specific networks in the brain that indicate a patient has or is at risk for Parkinson's disease and other neurodegenerative disorders.

"One of the major challenges in developing new treatments for neurodegenerative disorders such as Parkinson's disease is that it is common for patients participating in clinical trials to experience a placebo or sham effect," noted Dr. Eidelberg. "When patients involved in a clinical trial commonly experience benefits from placebo, it's difficult for researchers to identify if the treatment being studied is effective. In a new study conducted by my colleagues and myself, we have used a new image-based strategy to identify and measure placebo effects in brain disorder clinical trials."

In the current study, the researchers used their network mapping technique to identify specific brain circuits underlying the response to sham surgery in Parkinson's disease patients participating in a gene therapy trial. The expression of this network measured under blinded conditions correlated with the sham subjects' clinical outcome; the network changes were reversed when the subjects learned of their sham treatment status. Finally, an individual subject's network expression value measured before the treatment predicted his/her subsequent blinded response to sham treatment. This suggests that this novel image-based measure of the sham-related network can help to reduce the number of subjects assigned to sham treatment in randomized clinical trials for brain disorders by excluding those subjects who are more likely to display placebo effects under blinded conditions.