Currently, depression is diagnosed by a survey — commonly, the Hamilton Depression Rating Scale (HDRS) — which quantifies the severity of the illness in patients.
This method sums up the patient’s total depression into a single score based on how they answer questions about their symptoms. However, two patients with the same HDRS scores could have very different symptoms.
The subjective nature of the diagnostic survey contributes to the trial and error process of finding the right depression treatment for a specific individual.
To that end, researchers at Stanford University set out to develop a more effective method.
In a recent study, the Stanford researchers trained an algorithm to predict the extent to which a patient’s individual symptoms would improve with antidepressants based on their HDRS scores and brainwaves.
Predicting the effect of antidepressants on individual symptoms — and not treating their depression as a single, unidimensional condition — could lead to a more effective treatment path, according to the researchers.
“We’ve found that brainwave measurements can be used to help identify which particular symptoms change with antidepressant treatment and which do not,” [psychiatrist Leanne] Williams, who was involved in the study, said.
“It can be devastating for a patient when an antidepressant doesn’t work,” UT Southwest researcher Madhukar Trivedi said in a press release. “Our research is showing that they no longer have to endure the painful process of trial and error.”