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Researchers Use Brain Scans to Predict Success of Anti-Depressants

FUNCTIONAL magnetic resonance imaging (FMRI) scans and cognitive tests can predict treatment response in patients with major depression, according to the findings of a study from the University of Illinois at Chicago (UIC), Illinois, USA and the University of Michigan, Michigan, USA.

The findings could allow clinicians to determine whether a patient would be responsive to an anti-depressant therapy prior to them beginning a lengthy course of medication. This would also allow patients to recover faster by avoiding ineffective therapy and reduce the costs of unnecessary treatments for healthcare providers.

In the study, researchers found that a high activity in two particular networks of the brain when patients made mistakes during a cognitive test was a predictor of being less likely to respond to medication. The two networks were the error detection network, which becomes active when someone becomes aware of making a mistake, and the interference processing network, which activates when deciding what information to focus on. “We believe that increased crosstalk within these networks may reflect a propensity to ruminate on negative occurrences, such as a 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,” said Natania Crane, UIC, first author of the study.

Crane and the team carried out fMRI scans on 36 adult patients with major depressive disorder as they completed a task requiring them to watch letters flash across a screen. The participants were asked to press a button each time they saw a letter but not to press the button a second time if the letter was repeated. Each patient also received either escitalopram or duloxetine anti-depressants for the course of the study. Patients were followed-up after 10 weeks to determine whether the medication had relieved their symptoms.

The team found that patients whose brain activity was higher in the two networks were less likely to experience a reduction in symptoms and this was in line with their own predictions. “Using our model, we were able to predict with a very high degree of accuracy, in fact 90%, which patients would respond well to anti-depressant treatment, and which would not,” said Prof Scott Langenecker, UIC, corresponding author of the paper. “This is an important step towards individualised medicine for depression treatment. Using cognitive tests and fMRI, we can identify who will respond best to anti-depressant therapy and who may need other effective therapies that work through different mechanisms, like psychotherapy.”

Jack Redden, Reporter

Keywords: Antidepressants, Depression, Error Detection, fMRI, Interference Processing