Newswise — There is growing evidence that electroencephalograph (EEG) measurements can do more than just record the electrical activity in our brains—they can actually help predict how we will respond to specific medical treatments. New fuel for this idea came in a recent study published in PLOS Computational Biology. A team of researchers found that brain signals recorded via EEG could reveal whether a person given the anesthetic propofol would slip into unconsciousness easily or fight the drug: patients exhibiting strong alpha waves resisted the propofol while those whose EEGs revealed weaker alpha waves succumbed more easily. These results bring scientists closer to being able to tailor doses of anesthetics for specific patients rather than using a one-size-fits-all attitude. This type of advance reflects researchers’ ongoing focus on learning as much as possible about our brains, and seeing how brain behavior can be analyzed to help us develop better tools for healthcare.
George Carpenter, president and CEO of Mission Viejo, CA-based MYnd Analytics, Inc., is well aware of the potential of EEG results to serve as a vital predictive tool in evidence-based medicine. He is specifically interested in its potential to better serve those who are seeking treatment for mental health issues.
Many will agree that finding the right medication customized for each patient with mental health issues is one of the greatest gaps in modern healthcare. With more than 130 psychotropic drugs to choose from, it’s difficult for a physician to know which regimen is best. Clinicians must often resort to general guidelines, but ultimately trial and error before finding a treatment regimen that works, often subjecting patients to weeks of ineffective treatments. Apart from the patient not receiving relief from their disease while trying to find the right treatment, this extended “trial period” can expose patients to significant side effects—including the potential increase of suicidality. In one type of mental health issue alone, depression, a comprehensive study by the Agency for Healthcare Research and Quality found 40 percent of patients treated for depression ultimately failed to respond to any of the treatments they were given. Clearly, there is a profound need to eliminate the guesswork and offer evidence-based guidance to help patients.
MYnd Analytics has developed just such a tool: its PEER Online platform. Short for “Psychiatric EEG Evaluation Registry”, PEER works as follows. First, a doctor gives a patient an EEG test to measure the patient’s brain patterns. These data are analyzed and then compared with MYnd Analytics’ proprietary PEER database. This database contains more than 38,000 clinical endpoints from more than 10,000 patients who were also given EEGs and who responded to specific medications for their mental health—some positively, some negatively.
By looking for the closest match between the patient’s EEG and those EEGs already in the database, the doctor can review these “crowdsourced” results, which can help guide the selection of a regimen that is statistically most likely to help his patient and avoid those treatments that are less likely to help—or maybe choose none at all (as has sometimes been the case). This may help reduce the number of prescribed medications that ultimately end up in the toilet or at the back of the medicine cabinet because patients don’t find them helpful. More than 100 studies to date have demonstrated the correlation between EEGs and medication response.
If further studies corroborate PEER’s ability to accurately match patients with the most effective treatments, the age of depending solely on trial and error in prescribing medication for mental health might someday be a thing of the past.
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