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An electroencephalographic signature predicts antidepressant response in major depression

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 co...

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Autores principales: Wu, Wei, Zhang, Yu, Jiang, Jing, Lucas, Molly V., Fonzo, Gregory A., Rolle, Camarin E., Cooper, Crystal, Chin-Fatt, Cherise, Krepel, Noralie, Cornelssen, Carena A., Wright, Rachael, Toll, Russell T., Trivedi, Hersh M., Monuszko, Karen, Caudle, Trevor L., Sarhadi, Kamron, Jha, Manish K., Trombello, Joseph M., Deckersbach, Thilo, Adams, Phil, McGrath, Patrick J., Weissman, Myrna M., Fava, Maurizio, Pizzagalli, Diego A., Arns, Martijn, Trivedi, Madhukar H., Etkin, Amit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145761/
https://www.ncbi.nlm.nih.gov/pubmed/32042166
http://dx.doi.org/10.1038/s41587-019-0397-3
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author Wu, Wei
Zhang, Yu
Jiang, Jing
Lucas, Molly V.
Fonzo, Gregory A.
Rolle, Camarin E.
Cooper, Crystal
Chin-Fatt, Cherise
Krepel, Noralie
Cornelssen, Carena A.
Wright, Rachael
Toll, Russell T.
Trivedi, Hersh M.
Monuszko, Karen
Caudle, Trevor L.
Sarhadi, Kamron
Jha, Manish K.
Trombello, Joseph M.
Deckersbach, Thilo
Adams, Phil
McGrath, Patrick J.
Weissman, Myrna M.
Fava, Maurizio
Pizzagalli, Diego A.
Arns, Martijn
Trivedi, Madhukar H.
Etkin, Amit
author_facet Wu, Wei
Zhang, Yu
Jiang, Jing
Lucas, Molly V.
Fonzo, Gregory A.
Rolle, Camarin E.
Cooper, Crystal
Chin-Fatt, Cherise
Krepel, Noralie
Cornelssen, Carena A.
Wright, Rachael
Toll, Russell T.
Trivedi, Hersh M.
Monuszko, Karen
Caudle, Trevor L.
Sarhadi, Kamron
Jha, Manish K.
Trombello, Joseph M.
Deckersbach, Thilo
Adams, Phil
McGrath, Patrick J.
Weissman, Myrna M.
Fava, Maurizio
Pizzagalli, Diego A.
Arns, Martijn
Trivedi, Madhukar H.
Etkin, Amit
author_sort Wu, Wei
collection PubMed
description 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 (rsEEG) 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 repetitive transcranial magnetic stimulation (rTMS) treatment outcome. Furthermore, we found that the sertraline rsEEG signature indexed prefrontal neural responsivity, as measured by concurrent TMS/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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spelling pubmed-71457612020-08-10 An electroencephalographic signature predicts antidepressant response in major depression Wu, Wei Zhang, Yu Jiang, Jing Lucas, Molly V. Fonzo, Gregory A. Rolle, Camarin E. Cooper, Crystal Chin-Fatt, Cherise Krepel, Noralie Cornelssen, Carena A. Wright, Rachael Toll, Russell T. Trivedi, Hersh M. Monuszko, Karen Caudle, Trevor L. Sarhadi, Kamron Jha, Manish K. Trombello, Joseph M. Deckersbach, Thilo Adams, Phil McGrath, Patrick J. Weissman, Myrna M. Fava, Maurizio Pizzagalli, Diego A. Arns, Martijn Trivedi, Madhukar H. Etkin, Amit Nat Biotechnol Article 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 (rsEEG) 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 repetitive transcranial magnetic stimulation (rTMS) treatment outcome. Furthermore, we found that the sertraline rsEEG signature indexed prefrontal neural responsivity, as measured by concurrent TMS/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. 2020-02-10 2020-04 /pmc/articles/PMC7145761/ /pubmed/32042166 http://dx.doi.org/10.1038/s41587-019-0397-3 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Wu, Wei
Zhang, Yu
Jiang, Jing
Lucas, Molly V.
Fonzo, Gregory A.
Rolle, Camarin E.
Cooper, Crystal
Chin-Fatt, Cherise
Krepel, Noralie
Cornelssen, Carena A.
Wright, Rachael
Toll, Russell T.
Trivedi, Hersh M.
Monuszko, Karen
Caudle, Trevor L.
Sarhadi, Kamron
Jha, Manish K.
Trombello, Joseph M.
Deckersbach, Thilo
Adams, Phil
McGrath, Patrick J.
Weissman, Myrna M.
Fava, Maurizio
Pizzagalli, Diego A.
Arns, Martijn
Trivedi, Madhukar H.
Etkin, Amit
An electroencephalographic signature predicts antidepressant response in major depression
title An electroencephalographic signature predicts antidepressant response in major depression
title_full An electroencephalographic signature predicts antidepressant response in major depression
title_fullStr An electroencephalographic signature predicts antidepressant response in major depression
title_full_unstemmed An electroencephalographic signature predicts antidepressant response in major depression
title_short An electroencephalographic signature predicts antidepressant response in major depression
title_sort electroencephalographic signature predicts antidepressant response in major depression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145761/
https://www.ncbi.nlm.nih.gov/pubmed/32042166
http://dx.doi.org/10.1038/s41587-019-0397-3
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