EEG-Based Model Offers Hope for Personalized Depression Treatment: JAMA

Untreated depression has become an escalating public health concern, with sufferers often enduring a lengthy trial-and-error process to find effective treatments. Utilizing electroencephalography offers a dependable means of anticipating how individuals with depression will respond to particular antidepressant drugs, potentially guiding the selection of the most suitable treatment for these patients. The outcomes were published in the Journal of American Medical Association.

have developed an electroencephalography (EEG) based predictive model that could forecast the response of individuals suffering from depression to two different selective serotonin reuptake inhibitor (SSRI) medications. The study tapped into EEG data collected between 2011 and 2017 from two independent cohorts: one from the Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium.

Participants, aged 18 to 65, all diagnosed with major depressive disorder, were included in the study. The data collected was rigorously analyzed throughout 2022.

The CAN-BIND group underwent an 8-week treatment regimen of escitalopram, while the EMBARC participants were part of a double-blind trial and received an 8-week sertraline treatment or a placebo. The model's performance was assessed using balanced accuracy, specificity, and sensitivity metrics.

During internal validation with the CAN-BIND cohort, the model achieved a balanced accuracy of 64.2%, a sensitivity of 66.1%, and specificity of 62.3%. The external validation with the EMBARC group showed similarly promising results, with a balanced accuracy of 63.7%, sensitivity of 58.8%, and specificity of 68.5%. For the EMBARC placebo group, the model maintained a specificity of 47.3%, indicating its effectiveness in predicting response specifically to SSRIs.

These results suggest that EEG-based models could revolutionize the way depression is treated by helping clinicians predict which SSRI medication is most likely to be effective for an individual patient. While further research and validation are needed, this EEG-based model offers a glimmer of hope for the millions of people worldwide suffering from depression, paving the way for a brighter future with more targeted and effective treatments.

Reference:

Schwartzmann, B., Dhami, P., Uher, R., Lam, R. W., Frey, B. N., Milev, R., Müller, D. J., Blier, P., Soares, C. N., Parikh, S. V., Turecki, G., Foster, J. A., Rotzinger, S., Kennedy, S. H., & Farzan, F. (2023). Developing an electroencephalography-based model for predicting response to antidepressant medication. JAMA Network Open, 6(9), e2336094–e2336094. https://doi.org/10.1001/jamanetworkopen.2023.36094