Abstract
Recent meta-analyses point to the relatively low efficacy of commonly used antidepressant medications. Selecting the most effective medications for depressed subjects having failed previous treatments is especially difficult. There is a clear need for objective biomarkers that could assist and optimize such treatment selection. We will review here a growing body of evidence suggesting that several electroencephalography (EEG)-based methods may be useful for predicting antidepressant response and eventually for guiding clinical treatment decisions. While most of these methods are based on resting-state EEGs (e.g., alpha- and theta-band EEG abnormalities, the combined Antidepressant Response Index (ATR), cordance, referenced EEG), others include EEG source localization and evoked potentials. The limitations of these technologies and the potential clinical uses will also be outlined.