Patients suffering from depression can be forced to seek all types of treatments for months before pinning down an efficient one. But now, a team of researchers discovered that brain wave patterns could most likely help to predict how patients would react to an antidepressant before they are attributed to a particular treatment.
The new study has been published on February 10th in the journal Nature Biotechnology. It focused on one of psychiatry’s most challenging part: the lack of tests that can aid doctors to decide the ideal treatment options for patients suffering from depression.
Rather, providers depend on a trial-and-error process in which people try out drugs for about six to eight cycles, study co-author Dr. Madhukar Trivedi, a psychiatry professor at UT Southwestern Medical Center in Dallas, said.
This inaccurate technique adds to a general idea that antidepressants are not effective, according to Dr. Amit Etkin, the study co-author and professor of psychiatry at Standford University. However, a precise predictor of a patient’s perfect treatment could eliminate a lot of guesswork, and save them of months of frustration, said Katie Burkhouse, an assistant professor of psychiatry at the University of Illinois, who was not part of the research team that composed this study. The new research is ‘an important first step’ in getting closer to that aim, Burkhouse said.
For the research, the team of scientists collected brain wave recordings from over 300 patients who were suffering from depression. The recordings were registered with electroencephalography (EEG), an aseptic method that requires attaching electrodes to patients’ scalps. People are then randomly selected to be given either a placebo or the antidepressant sertraline, also known as Zoloft.
The Technology Could be Adapted for Doctors’ Offices
Based on the EEG data, scientists created a new artificial intelligence (AI) algorithm to predict patients’ reactions to the drug. They discovered that people with a particular brain wave pattern at the start of the research were prone to react positively to sertraline after wight weeks of medication. The team then used the algorithm to three extra patient data sets to confirm their discoveries.
The outcome “go against the prevailing wisdom that these drugs are just ineffective,” Etkin said. “They are actually quite effective, but only for a subpopulation of people.”
Even though this study’s results were promising, it is rather not clear whether the AI would be ideal to use in ‘real world’ clinical environments, Burkhouse stated.
The research particularly examined how patients reacted to sertraline, for instance, which is just one of the numerous possible treatments for depression.
“A next step for the study would be to test if [the algorithm] is predictive of other forms of treatment that aren’t necessarily just medication-based,” Burkhouse said. Other treatment types could be cognitive therapy and brain stimulation.
However, Etkin claimed that the technology could be adjusted for use in doctors’ offices, just like EEG has been utilized for decades now. Etkin said he hopes the results will help guide in ‘the beginning of precision psychiatry.’
Etkin is the founder and CEO of Alto Neuroscience, a startup that intends to create personalized mental health treatments.