An electroencephalographic signature predicts antidepressant response in major depression

作者：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

来源：Nature Biotechnology, 2020, 38(4): 439-+.

DOI：10.1038/s41587-019-0397-3

• TRANSCRANIAL MAGNETIC STIMULATION
• SINGLE-TRIAL EEG
• BRAIN-FUNCTION
• OSCILLATIONS
• MECHANISMS
• MODERATORS
• BIOMARKERS
• CONFLICT
• NETWORKS
• OUTCOMES

The efficacy of an antidepressant is predicted from an EEG signature. @@@ 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.