Can machine learning be used as a tool to predict depression treatment outcomes using brain activity?
Original Title:
Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression
Link: https://pubmed.ncbi.nlm.nih.gov/31899530/
Depression is a challenging condition to treat, and finding the right medication can be a lengthy and frustrating process. But what if we could predict the most effective treatment for an individual right from the beginning? Analyzing brain activity might hold the key to unlocking new insights that could significantly impact the way we address depression.
CAN-BIND researchers, Andrey Zhdanov, Faranak Farzan, and their colleagues, aimed to determine whether they could predict how individuals with depression would respond to a specific antidepressant medication called escitalopram. Escitalopram works by increasing levels of serotonin, a chemical messenger in the brain linked to mood regulation. It is commonly prescribed to reduce symptoms of depression and anxiety.
During the study, 180 individuals diagnosed with depression completed an 8-week treatment plan with escitalopram as part of the CAN-BIND-1 study. Using a technique called electroencephalography (EEG), the researchers then recorded each individual’s brain activity before and after the treatment. Advanced machine learning procedures were then used to analyze the EEG data and identify patterns that could predict treatment outcomes for individuals with depression receiving escitalopram.
The results demonstrated that by analyzing the EEG data, researchers could predict with a good level of accuracy how well someone might respond to escitalopram. With just the EEG data collected before treatment, the researchers were able to predict with approximately 79% accuracy which individuals would respond favorably to escitalopram. When the EEG data collected after 2 weeks of treatment was included in the analysis, the predictive accuracy improved to 82%. This suggests that analyzing EEG data could serve as a valuable tool to improve personalized treatments for depression, particularly with escitalopram therapy. More research in this area could help make the process of finding the right treatment for patients faster and more effective, improving outcomes for those struggling with depression.
Citation: Zhdanov A, Atluri S, Wong W, Vaghei Y, Daskalakis ZJ, Blumberger DM, et al. Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression. JAMA Netw Open 2020;3:e1918377. https://doi.org/10.1001/jamanetworkopen.2019.18377