{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:42:45Z","timestamp":1790887365145,"version":"4.1.0"},"reference-count":40,"publisher":"American Medical Association (AMA)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"Importance<\/jats:title>Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging.<\/jats:p><\/jats:sec>Objective<\/jats:title>To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications.<\/jats:p><\/jats:sec>Design, Setting, and Participants<\/jats:title>This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first 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. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022.<\/jats:p><\/jats:sec>Exposures<\/jats:title>In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment.<\/jats:p><\/jats:sec>Main Outcomes and Measures<\/jats:title>The model\u2019s performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity.<\/jats:p><\/jats:sec>Results<\/jats:title>The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model\u2019s specificity in predicting SSRIs treatment response.<\/jats:p><\/jats:sec>Conclusions and Relevance<\/jats:title>In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.<\/jats:p><\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.36094","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T15:37:41Z","timestamp":1695915461000},"page":"e2336094","source":"Crossref","is-referenced-by-count":24,"title":["Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication"],"prefix":"10.1001","volume":"6","author":[{"given":"Benjamin","family":"Schwartzmann","sequence":"first","affiliation":[{"name":"eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prabhjot","family":"Dhami","sequence":"additional","affiliation":[{"name":"eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, 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