US researchers say a computer can accurately predict whether an antidepressant will work based on a patient’s brain activity.
It’s the “flagship finding” of a national trial to better understand mood disorders involving major institutions from across the country and led by UT Southwestern (UTSW).
More than 300 people with depression were involved. All were randomly chosen to receive either a placebo or an SSRI (selective serotonin reuptake inhibitor), the most common class of antidepressant. An electroencephalogram (EEG) was then used to measure electrical activity in their cortex before they began treatment.
UTSW psychiatrist Madhukar Trivedi worked with Amit Etkin from Stanford University to develop a machine-learning algorithm to analyse and use the EEG data to predict which patients would benefit from the medication within two months.
Not only did the AI accurately predict outcomes, the researchers say, but further research suggested patients who were unlikely to respond to an antidepressant were likely to improve with other interventions such as psychotherapy or brain stimulation. The findings were validated in three additional patient groups.
“These studies have been a bigger success than anyone on our team could have imagined,” Trivedi says. “We provided abundant data to show we can move past the guessing game of choosing depression treatments and alter the mindset of how the disease should be diagnosed and treated.”
Among the next steps, the researchers say, is to develop an AI interface that can be widely integrated with EEGs across the country.
Data from the study came from a 16-week trial run as part of the national EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) program to establish biology-based, objective strategies to remedy mood disorders.
The trial evaluated patients with major depressive disorder through brain imaging and various DNA, blood, and other tests.
“We went into this thinking, ‘Wouldn’t it be better to identify at the beginning of treatment which treatments would be best for which patients?'” Trivedi explains.
Previous EMBARC studies identified various predictive tests, including the use of magnetic resonance imaging (MRI) to examine brain activity in both a resting state and during the processing of emotions.
EEG will likely be the most commonly used tool, Trivedi says, because it’s less expensive and – in most cases – will be equally or more effective. However, a blood test or MRI may be needed for some patients if the depression is manifesting itself in a different way.
“There are many signatures of depression in the body,” Trivedi says. “Having all these tests available will improve the chances of choosing the right treatment the first time.”
The new study is published in the journal Nature Biotechnology.