Depression is a debilitating condition that has far-reaching implications for patients and their families. Although 7% of Americans are familiar with the “black dog” and its ability to chain people in place, treatment options are lacking and imprecise. Different people respond differently to various antidepressants, reacting best to a specific type that may not be the initially prescribed plan of treatment. The average American with depression must try two to three antidepressants before finding one that works for them, and about one-third of patients are resistant to commonly prescribed medicines. The process of trying a medication and realizing it does not work usually lasts about eight weeks, causing distress in patients that often further exacerbates) depression symptoms. However, a recent study published in Nature Biotechnology may provide the first hints towards a solution: using technology to match people with the specific medications that match their body chemistry, increasing treatment efficiency and building greater trust between those with mental illness and their doctors.
The new study, which was conducted by researchers at Stanford, studied the brain waves of 309 subjects with clinical depression. The scientists used electroencephalography (EEG), to look at electrical brain signals to see if any markers corresponded to a tendency to respond a certain way to Zoloft (sertraline), a common antidepressant that achieves mixed results across patients. This technique only costs $50 to $100, a cost-effective option considering the thousands of dollars needed to conduct fMRI tests. The results can also be seen quickly, enabling patients to have answers before they leave the psychiatrist’s office.
The scientists found that one particular pattern of electrical activity correlated with how well the patients responded to Zoloft. A computer algorithm was able to use this relationship to predict the efficacy of this treatment for patients correctly in over 60% of patients.
A significant amount of error still exists in this technique, and current studies only indicate the ability to link electrical brain signals with a given patient’s future responses to Zoloft. However, these studies represent the first steps into a vastly different framework for prescribing patients with depression medications that are best matched for their bodies rather than performing trial and error. If other signals are found that correlate to certain responses to other medications, a psychiatrist could order a test and upload it to a source with the ability to analyze the results and provide an effective medication on the first try, or at least with much higher accuracy. This will not only help patients feel better faster, but potentially help other people with symptoms of depression gain trust in psychiatrists and reach out for help.
Study author Amit Etkin, a professor of psychiatry and behavioral sciences at Stanford, is taking leave from his university position to found Alto Neuroscience, a company that is extending upon his research to develop “biological tests to personalize treatment for conditions like depression”. He is very excited about his research’s success and sees enough potential in it that he is putting his finances in jeopardy to pursue his vision with full force. He claims that an EEG test is “something that could be done very quickly and easily in any clinic, and then you can get your result by the time you leave the office”.
Until these experimental methods are further developed and released for clinical use, patients must continue testing medications via a long process that occasionally takes years to provide them with any relief. However, these studies should be a source of hope in a modernizing world where mental health conditions such as depression, caused by environmental or chemical factors, are on the rise and providing significant risk to millions of people and their families.
Disclaimer: The views and opinions expressed by individual writers in the opinion section do not reflect the views and opinions of The Daily Campus or other staff members. Only articles labeled “Editorial” are the official opinions of The Daily Campus.
Katherine Lee is a staff columnist for The Daily Campus. She can be reached at firstname.lastname@example.org.