Through functional MRI (fMRI) and machine learning technology, UCLA researchers have developed a way to predict whether individuals with obsessive compulsive disorder (OCD) will benefit from cognitive behavioral therapy (CBT), according to a recent UCLA release.  

“If the results of this study are replicated in future studies, the methods we used could potentially give clinicians a new predictive tool,” said lead author Nicco Reggente, a UCLA doctoral student, in a prepared statement. “If a patient is predicted to be a non-responder to cognitive behavioral therapy, clinicians could pursue different options.” 

The study, published Feb. 12 in the journal Proceedings of the National Academy of Sciences, involved conducting fMRI brain exams of 42 individuals with OCD between the ages of 18 and 60. Scans were taken before and after four weeks of intensive CBT. Researchers analyzed functional connectivity within the brain through measuring blood flow with MRI technology. The severity of participants' OCD symptoms was also assessed. Data from fMRI scans and symptom assessments were put into a computerized machine learning program to determine which participants would respond to CBT treatment.  

“This method opens a window into OCD patients’ brains to help us see how responsive they will be to treatment,” said co-author of the study Jamie Feusner, MD, a neuroscientist at the Semel Institute for Neuroscience and Human Behavior at UCLA, in the statement. “The algorithm performed far better than our own predictions based on their symptoms and other clinical information.” 

See the entire press release below for more information and study findings.