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An AI can predict from people’s brainwaves whether an antidepressant is likely to help them. The technique may offer a new approach to prescribing medicines for mental illnesses.
Antidepressants don’t always work, and we aren’t sure why. “We have a central problem in psychiatry because we characterise diseases by their end point, such as what behaviours they cause,” says Amit Etkin at Stanford University in California. “You tell me you’re depressed, and I don’t know any more than that. I don’t really know what’s going on in the brain and we prescribe medication on very little information.”
Etkin wanted to find out if a machine-learning algorithm could predict from the brain scans of people diagnosed with depression who was most likely to respond to treatment with the antidepressant sertraline. The drug is typically effective in only a third of the people who take it.
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He and his team gathered electroencephalogram (EEG) recordings showing the brainwaves of 228 people aged between 18 and 65 with depression. These individuals had previously tried antidepressants, but weren’t on such drugs at the start of the study.
Roughly half the participants were given sertraline, while the rest got a placebo. The researchers then monitored the participants’ mood over eight weeks, measuring any changes using a depression rating scale.
Brain activity patterns
By comparing the EEG recordings of those who responded well to the drug with those who didn’t, the machine-learning algorithm was able to identify a specific pattern of brain activity linked with a higher likelihood of finding sertraline helpful.
The team then tested the algorithm on a different group of 279 people. Although only 41 per cent of overall participants responded well to sertraline, 76 per cent of those the algorithm predicted would benefit did so.
Etkin has founded a company called Alto Neuroscience to develop the technology. He hopes it results in more efficient sertraline prescription by giving doctors “the tools to make decisions about their patients using objective tests, decisions that they’re currently making by chance”, says Etkin.
This AI “could have potential future relevance to patients with depression”, says Christian Gluud at the Copenhagen Trial Unit in Denmark. But the results need to be replicated by other researchers “before any transfer to clinical practice can be considered”, he says.
Journal reference: Nature Biotechnology, DOI: 10.1038/s41587-019-0397-3
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