Stanford Medicine News Center notes EEG can predict who benefits from antidepressants

The Sinha Clinic has provided medication sensitivity testing (Referenced EEG) for years to the Saint Charles, Illinois and surrounding area in determining the best psychotropic medication for one’s brain chemistry. 

An article published in February 2020 by Stanford Medicine news center, “Stanford researchers and collaborators used electroencephalography, or EEG, a tool for monitoring electrical activity in the brain, and an algorithm to identify a brain-wave signature in individuals with depression who will most likely respond to sertraline, an antidepressant marketed as Zoloft.” 

The study noted “interpreting brain activity would be used in clinics to help determine the best treatment options for depression.”

The Sinha Clinic has used medication sensitivity testing (Referenced EEG) to narrow down the top medications for one’s brain chemistry for many years in the Saint Charles, IL and surrounding suburbs.  Stanford Medicine news center noted “emerged from a decades-long effort funded by the National Institute of Mental Health to create biologically based approaches, such as blood tests and brain imaging, to help personalize the treatment of depression and other mental disorders. Currently, there are no such tests to objectively diagnose depression or guide its treatment.” 

Medication sensitivity testing (Referenced EEG) does not need to supply a blood sample to complete this test. A brain map is completed and analyzed in a normative database. This is done in office and takes approximately a week to receive results.

The article further noted “instead of functional magnetic resonance imaging, an expensive technology often used in studies to image brain activity, the scientists turned to electroencephalography, or EEG, a much less costly technology.”   

“This study takes previous research showing that we can predict who benefits from an antidepressant and actually brings it to the point of practical utility,” said Amit Etkin, MD, PhD, professor of psychiatry and behavioral sciences at Stanford. “I will be surprised if this isn’t used by clinicians within the next five years.” 

Further findings note, “Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.” This innovative technology reduces the trial and error method used by physicians to narrow down the best psychotropic medications to the top ones that fit a persons specific brain chemistry. This saves time, money and reduces the mental frustration from trial and error method used during traditional medication management. 


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