TY - JOUR
T1 - An electroencephalographic signature predicts antidepressant response in major depression
AU - Wu, Wei
AU - Zhang, Yu
AU - Jiang, Jing
AU - Lucas, Molly V.
AU - Fonzo, Gregory A.
AU - Rolle, Camarin E.
AU - Cooper, Crystal
AU - Chin-Fatt, Cherise
AU - Krepel, Noralie
AU - Cornelssen, Carena A.
AU - Wright, Rachael
AU - Toll, Russell T.
AU - Trivedi, Hersh M.
AU - Monuszko, Karen
AU - Caudle, Trevor L.
AU - Sarhadi, Kamron
AU - Jha, Manish K.
AU - Trombello, Joseph M.
AU - Deckersbach, Thilo
AU - Adams, Phil
AU - McGrath, Patrick J.
AU - Weissman, Myrna M.
AU - Fava, Maurizio
AU - Pizzagalli, Diego A.
AU - Arns, Martijn
AU - Trivedi, Madhukar H.
AU - Etkin, Amit
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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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U2 - 10.1038/s41587-019-0397-3
DO - 10.1038/s41587-019-0397-3
M3 - Article
C2 - 32042166
AN - SCOPUS:85079454651
SN - 1087-0156
VL - 38
SP - 439
EP - 447
JO - Nature biotechnology
JF - Nature biotechnology
IS - 4
ER -