Background Major depressive disorder (MDD) is a heterogeneous neuropsychiatric disorder with highly variable antidepressant outcomes. In randomized controlled trials (RCTs), low drug-placebo differences and high placebo response rates are persistent challenges. An objective biomarker that can prospectively identify which patients will respond to antidepressant or placebo could greatly enhance both clinical care and clinical trial outcomes.

Methods Baseline scalp EEG data from EMBARC, a multi-site RCT of the SSRI sertraline vs placebo in adult MDD, were analyzed using unsupervised machine learning to identify subtypes and compare these with their corresponding treatment response profiles. Subtypes response to sertraline versus placebo was evaluated by 8-week HAMD-17 outcomes (change from baseline).

Results Of the 215 subjects, three EEG clusters yielded four response phenotypes. (1) Drug–Responders exhibited a large sertraline advantage over placebo (n = 124; d = 1.23; p < 0.0001). (2) Non–Responders derived no benefit from sertraline (n = 37; d = –0.07; p = 0.84). (3) Divergent–Responders shared a distinctive connectivity profile clearly separable from phenotypes 1 and 2. Within this group, participants randomized to placebo improved robustly (Placebo–Responders; n = 54; d = –1.52; p < 0.0001), whereas those receiving sertraline worsened (Adverse Drug–Responders; n = 31; d = -0.67; p = 0.004). Excluding Placebo–Responders more than tripled the overall drug–placebo effect size (d = 0.89 vs 0.28). Cluster membership was highly stable in 10–fold cross–validation (98–99 % consistency) and reproduced across three independent trial sites, underscoring generalizability.

Conclusions Scalp EEG activity analyzed with machine learning identified four biomarker-defined subtypes with strikingly distinct responses to an antidepressant and placebo. These results raise the possibility of using low-cost, noninvasive EEG to guide personalized treatment decisions, avoid ineffective or harmful medications, and improve clinical trial outcomes by identifying drug and high placebo responders in advance of initiating treatment.

Drs. Li, Zhang, and Wang are full-time employees at Neumarker Inc. and have nothing else to declare. Dr. Detke has served as a consultant to Adial, Catalys, Lighthouse, Neumarker, Neumora, and the NIH during the past twelve months. Dr. Potter has consulted to Karuna, Neurocrine, Neumarker, Praxis Bioreserach, Theravance, and Vaaji during the past twelve months. Dr. Breier has consulted to BioXcell, Karuna, Neumarker, and Terran during the past twelve months. Dr. Alphs has consulted to Denovo Biopharma, Netramark, Neumarker and Roche Genentech during the past twelve months. Dr. Wolkowitz has consulted to Neumarker. Dr. Ereshefsky has consulted to Clexio, CenExcel Research, Damona, Neumarker, Proscience Research Group, Reviva, Q-Mtrx, Vandria, and Viage during the past twelve months. Dr. Grecco has served as a consultant to Neumarker Inc.

While the original clinical trial was funded via the NIMH, this secondary analysis was funded by Neumarker Inc.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study used ONLY openly available human data that were originally located at: the NIMH Data Archive (#2199)

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The data analyzed in the present study is publicly available at the NIMH Data Archive. The data produced from current study are not publicly available due to ongoing commercial development. A de-identified limited subset of data may be made available upon reasonable request and subject to a data use agreement.

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