| dc.description.abstract | Backround: Major Depressive Disorder (MDD) is considered to be an enormous burden on society, so much that it is considered to be the largest non-fatal disorder. Antidepressants nowadays are considered to be the most popular treatment for this disorder , however there is a substantial number of patients who either respond much later to pharmacotherapy or not at all. In order to predict response to pharmaceutical treatment, emphasis should be given to biomarkers. Electroencephalography (EEG) seems like a promising and accessible neuroimaging technique to optimize treatment by detecting early on which MDD patients will be better responders. This meta analysis presents a synthesis of studies correlating the degree of treatment response with pretreatment EEG frequency.
Methods: PubMed, Psycinfo and Embase were searched for relevant studies and longitudinal studies were included in the meta analysis if they used MDD samples, patients underwent a washout period for current medications before receiving new treatment, antidepressants were used as the only way of treatment and EEG measurement was performed before and after the treatment.
Results: 16 studies met the inclusion criteria. Three of them were excluded because of missing data. When the data from the remaining 13 studies were combined, a small to medium pooled effect size was observed. Because of the high heterogeneity among studies, a subgroup analysis was performed with EEG rhythm and age used as moderators. Theta rhythm was found to be a significant predictor for better antidepressant response.
Conclusion: In accordance with previous findings, this meta-analysis showed that higher pretreatment theta rhythm was a possible predictor of better antidepressant response. Potentially pretreatment activity in the rostral anterior cingulate cortex (ACC) can be a reliable predictor, especially given that this area is the main generator of theta activity. Recommendations for future research include integration of EEG to a variety of other biological factors in order to predict the response to antidepressants more accurately. | |
