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An electroencephalographic signature predicts antidepressant response in major depression.
- Source :
- Nature biotechnology; vol 38, iss 4, 439-447; 1087-0156
- Publication Year :
- 2020
-
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.
Details
- Database :
- OAIster
- Journal :
- Nature biotechnology; vol 38, iss 4, 439-447; 1087-0156
- Notes :
- application/pdf, Nature biotechnology vol 38, iss 4, 439-447 1087-0156
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1391579849
- Document Type :
- Electronic Resource