Objectives This study compared machine learning models using unimodal imaging measures
and combined multi-modal imaging measures for deep brain stimulation (DBS) outcome …

… with later clinical response to antidepressant treatment. However, a small cluster of patients
(N = 28) … phenotypes (eg, neurophysiological or neurochemical characteristics) and whether …

… depressive symptoms, improving anhedonia and motivation. We have observed rapid
antidepressant response … and glucose metabolism predicted eventual clinical response, but in …

… predict a better response to antidepressant drug … clinical features as well as neurophysiological
measures (ie, seizure threshold and duration) that might have an influence on the clinical …

… Major depressive disorder (MDD) is one of the most prevalent … , many patients do not
respond to traditional antidepressant … would predict response with a high degree of accuracy. …

… predicting clinical response … depression focused on EEG (10) or MRI (11) and a critical
meta-analysis questioned the usefulness of EEG biomarkers for guiding antidepressant response …

… objective behavioral and neurophysiological measurement of … in precision medicine for
predicting future clinical outcomes … to predict early response to antidepressant treatment in drug-…

… , and its associations with clinical response and with depressive cognitive distortions, as
measured … predictor of antidepressant response to be used in a wide variety of clinical settings. …

… treatment could predict response to antidepressants and … neurophysiological indices for
the selection of treatments and for predicting the response to them, 17 helping clinical practice …

… antidepressants, leading to treatment-resistant depression (TRD). We evaluated the
antidepressant … Baseline BDNF levels of our patient sample predicted response at one- and four-…