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Cited by 302Treatment of Major DepressionTranscranial Magnetic Stimulation StudiesNeural and Behavioral Psychology Studies

An electroencephalographic signature predicts antidepressant response in major depression

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Wu, Wei, Zhang, Yu, Jiang, Jing, Lucas, Molly V., Fonzo, Gregory A., Rolle, Camarin E., Cooper, Crystal, Chin-Fatt, Cherise +19 more·2020·Nature Biotechnology
DOI 10.1038/s41587-019-0397-3PMC PMC7145761
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Abstract

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

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  • AI agents show significant potential in personalized treatment by leveraging patient-specific data to tailor individualized treatment plans that optimize therapeutic outcomes.

    “AI agents show significant potential in the realm of personalized treatment by leveraging a variety of patient-specific data, including genetic information, medical history, lifestyle factors, and treatment responses.77,88,89,90,91,92”

    From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [92]

    The Discussion (page unavailable) says that an individual-level EEG signature may support personalized treatment selection for depression. The Online Methods and Results (pages unavailable) describe predicting antidepressant response primarily from EEG data, not AI agents combining genetic information, medical history, lifestyle factors, and treatment responses to tailor plans. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

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Authors
Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C. A., Wright, R., Toll, R. T., Trivedi, H. M., Monuszko, K., Caudle, T. L., Sarhadi, K., Jha, M. K., Trombello, J. M., Deckersbach, T., Adams, P., McGrath, P. J., Weissman, M. M., Fava, M., Pizzagalli, D. A., Arns, M., Trivedi, M. H., & Etkin, A.
Affiliations
  • VA Palo Alto Health Care System (US)
  • The University of Texas Southwestern Medical Center (US)
  • Maastricht University (NL)
  • Harvard University (US)
  • New York State Psychiatric Institute (US)
  • Utrecht University (NL)
Corresponding author
Etkin, Amit
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2020-02-10
Volume
38
Issue
4
Pages
439-447
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/s41587-019-0397-3
PMC
PMC7145761
OpenAlex
W3005405338
Cited by
302
References
11
Citations by year
2026: 37 · 2025: 49 · 2024: 61 · 2023: 46 · 2022: 31 · 2021: 45 · 2020: 33
FWCI
23.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • Research Publications (Maastricht University) (submittedVersion)
Funders
  • National Natural Science Foundation of China (61876063, 61836003)
  • U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (DP1MH116506, U01MH092221, U01MH092250, R01MH103324)
  • Stanford Neurosciences Institute
Topics
  • Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
  • Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
major depressive disorder, antidepressant response prediction, resting-state EEG, sertraline, placebo response, transcranial magnetic stimulation, personalized treatment, treatment response biomarkers
MeSH terms
Machine Learning, Antidepressive Agents, Depressive Disorder, Major, Major Depressive Disorder, Double-Blind Method, Electroencephalography, Humans, Membrane Potentials, Models, Neurological, Predictive Value of Tests, Reproducibility of Results, Treatment Outcome, Prefrontal Cortex, Sertraline, Transcranial Magnetic Stimulation

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