Predicting patient responses to drugs could reduce the time it takes patients to begin feeling better and reduce health-care costs.
A functional MRI brain scan may help predict which patients will respond positively to antidepressant therapy, according to a new study published in the journal Brain.
Researchers at the University of Illinois at Chicago and the University of Michigan performed fMRI scans on patients with major depressive disorder who were to begin antidepressant therapy. Those patients who show more communication within two brain networks when they made a mistake while performing an assigned cognitive task were less likely to respond to antidepressant medication.
The two networks are the error detection network — which engages when someone notices they’ve made a mistake — and the interference processing network, which activates when deciding what information to focus on.
“We believe that increased cross-talk within these networks may reflect a propensity to ruminate on negative occurrences, such as mistake, or a deficit in emotional regulation when faced with a mistake, and our medications may be less effective in helping these types of patients,” says Natania Crane, a graduate student in psychiatry in the UIC College of Medicine who is first author on the study.
Related Articles
- The Most Important Lesson From 83,000 Brain Scans
- Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials.
- MRI Ups Diagnostic Accuracy of Fetal Brain Abnormalities
- Prenatal brain MRI of fetuses with Zika virus infection.
Several studies that used fMRI to identify discrete areas of the brain that are hyperactive or underactive in patients with major depressive disorder have suggested that neuroimaging may be useful for predicting a patient’s response to a particular pharmaceutical therapy.
In the current study, the researchers looked at patterns of brain activation while participants performed a cognitive-control task to see if they predicted response to drug treatment. They used a unique analysis technique to determine which areas of the brain that were highly active during the commission of errors on a cognitive task correlated with treatment response, and how the strength of communication within specific brain networks predicted treatment response.