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2020-09-15| Technology

Do You Need to Switch Antidepressants? Your Brainwaves Can Tell

by Tulip Chakraborty
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It’s estimated that in the US alone, 16.2 million adults or 7 percent of American adults suffer from depression (also known as major depression disorder or MDD) or have had at least one major depressive episode each year.

By T. Chakraborty, Ph.D.

The most common method of treatment is the use of antidepressants, normally Selective Serotonin Reuptake Inhibitors (SSRI’s), like Prozac and Fluoxetine. These drugs take weeks or months to show any effect in the body and in 50% of the cases, they need to be changed by the doctor as they don’t work. This uncertainty can lead patients to further depths of depression and thus a treatment therapy is mandated. Recently, scientists have discovered that by measuring brainwaves produced during rapid eye movement (REM) sleep, they can predict whether a patient suffering from depression will respond to a particular treatment type. This huge breakthrough will now enable patients to switch to a new treatment within a week rather than being in the dark for weeks and months, not knowing the outcome of their current medication.

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A trial led by Dr. Thorsten Mikoteit, from the University of Basel, conducted a randomized trial to use brain waves to predict the success of antidepressant measured by the standard Hamilton Depression Rating Scale. This study mainly aimed to see a reduction in symptoms of depression by 50%. A total of 37 patients with major depression were enrolled for the study. All the patients were treated with antidepressants but 22 of the patients had their details given to the psychiatrist in charge of treatment. All the patients had their brainwaves measured during REM sleep and the doctors in charge interpreted the brainwaves to see if the antidepressants were working. As determined by the brainwaves, the patients who were unlikely to show improvements were switched to other treatments. After 5 weeks post-treatment, 87.5% of patients had an improved response, whereas only 20% in the control group showed improvement.

Thorsten Mikoteit added that “This is a pilot study, but it shows fairly significant improvements. We have shown that by predicting the non-response to antidepressants, we were able to adapt the treatment strategy more or less immediately: this enables us to significantly shorten the average duration between the start of antidepressant treatment and response, which is vital especially for seriously depressed patients. It needs to be repeated with a larger group of patients to ensure that the results are consistent. Patients need to be in a situation where their REM sleep can be monitored, which requires more care than just giving the pill and waiting to see what happens. This means that the treatment monitoring will be more expensive, although we anticipate that it will be offset by giving the right treatment much earlier. We are working on ways of streamlining this. It does mean that we may be able to treat the most at-risk patients, for example, those at risk of suicide, much quicker than we can currently do. If this is confirmed to be effective, it will save lives”.

Although not involved in this study, Professor Catherine Harmer from the University of Oxford and ECNP Executive Committee member commented that “Most of the time, patients need to wait for around 4 weeks before they can tell if they are responding to a particular antidepressant or not. This is a hugely disabling and lengthy process and often a different treatment then needs to be started. The study results presented by Mikoteit are interesting and suggest that it may be possible to tell if a treatment is working much more quickly—even after a week of treatment—by using a physiological measure of response (REM sleeping pattern). If this is replicated in larger, blinded study then it would have enormous implications for the future treatment of individuals with depression”.

Related Article: VistaGen’s Antidepressant Nasal Spray Succeeds Phase 2a Trial

References:

1. https://www.ecnp.eu/

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