The biggest impediment to tackling the rise of depression may not be a lack of treatment options but matching each person to what will suit them best. A new test based on the patient's brainwaves offers the possibility of predicting what will suit an individual’s needs, rather than relying on painful trial and error.
Selective serotonin reuptake inhibitors (SSRIs) have been life-changing for millions of people with major depression. For millions more they've meant nothing but wasted time, nasty side effects, sometimes including heightened suicide risk, and (in America) a hefty bill. The value of assessing which group someone falls into could hardly be overstated. A paper in Nature Biotechnology suggests the quest may finally be getting there.
Stanford's Professor Amit Etkin heads a large team that used electroencephalography, better known as EEG, to measure the brainwaves of people with major depression. These were analyzed for features thought to predict SSRI response. When those who scored highly on these signatures were put on sertraline, an SSRI marketed as Zoloft, almost two-thirds reported a 50 percent or higher reduction in their symptoms. Among those whose brainwaves lacked these signatures, only one in five reported the same benefit.
Half the trial participants were put on placebos rather than sertraline. This group had virtually identical responses, irrespective of what the EEGs showed, demonstrating the signatures relate to SSRI response, rather than to an underlying likelihood of improvement.
The focus of the paper was on predicting SSRI, specifically sertraline, suitability. Nevertheless, the authors also found those without the SSRI-compatible brainwaves on average had stronger responses to transcranial magnetic stimulation (TMS). Although the popularity of TMS is rising, it is currently usually only prescribed for people who have tried multiple rounds of SSRIs without much benefit.
The paper is far from the first to report potential indicators of SSRI response, including an earlier version of the same test published two years ago. While demonstrating the idea’s potential, at that time the prediction rate wasn't good enough to be very clinically useful. Other techniques have also identified apparent predictors of response to SSRIs. However, some of these involved small samples that have not been replicated, while others relied on expensive fMRI scans.
Although the paper acknowledges some remaining weaknesses in the study that could be addressed in future, the large sample size and much stronger predictive power set it apart from previous efforts. “I will be surprised if this isn't used by clinicians within the next five years,” Etkin said in a statement.
More broadly, the findings challenge the one-size-fits-all approach to the treatment of depression that tends to dominate at the moment. "As a psychiatrist, I know these patients differ a lot," Etkin said. "But we put them all under the same umbrella, and we treat them all the same way."
The authors note, even aside from side effects, patients can become dejected from having their hopes raised and then dashed by trying a treatment that doesn't work for them.