A new pilot study has found that it is possible to analyze brainwaves during REM sleep to quickly determine whether a particular antidepressant is likely to work for any given patient suffering from depression. The findings may pave the way for more rapid treatment of major depression, which can — in some cases — take multiple months of trial-and-error to find the right solution.
Depression can be difficult to treat, particularly in people suffering from Major Depression Disorder. Antidepressants are the most common initial treatments, particularly SSRIs, but the way in which they work means there’s a delay before changes are observed. In many cases, this can mean putting the patient on a particular antidepressant, then waiting four long weeks to determine whether it may offer some relief.
Around half of the people who take an antidepressant for the first time will find that it doesn’t work, meaning that after a few weeks, they’ll then be switched to a different antidepressant, restarting the evaluation cycle. This may continue multiple times before an effective antidepressant is found, leaving the patient in turmoil and at risk in the meantime.
Some past research has focused on ways to reduce this timespan, including using AI to predict the best antidepressant for any given patient. The latest study on the topic looked at brainwaves produced during REM sleep, particularly prefrontal theta cordance.
A total of 37 patients diagnosed with Major Depression were evaluated in as little as one week after starting an antidepressant. Compared to those in the control group, the participants who had their medication adjusted based on brainwaves saw a far greater response to treatment.
Whereas only 20-percent of the control group had improved on antidepressants over five weeks, nearly 88-percent of the brainwave analysis participants saw improvement over the same time period, indicating that this may be an effective way to determine which drugs are most effective for patients. The findings pave the way for a larger study on the topic.