Title: EEG and ERP biomarkers for prediction of antidepressant treatment response
PhD-student: Cheng Teng Ip, NRU and H.Lundbeck A/S
The World Health Organization (WHO) has ranked Major Depresssive Disoreder (MDD) as the leading cause of disease burden, especially for women. MDD is characterized by a personal suffering and both social and functional disability and is often associated with high health care costs. Today, it is the sentiment that MDD rather than being a unitary condition may have multiple causes and disease mechanisms which probably is the reason why many patients do not have a satisfactory response to antidepressive treatment. This is clearly not satisfactory and calls for ways to classify MDD patients as well as defining biomarkers that can predict treatment outcome. There is some evidence that electrophysiological features can serve as a predictor for the treatment outcome and thus reduce the number of failed treatments. To date, different EEG/ERPs parameters have been suggested and explored as potential predictors of antidepressants outcome, with somewhat diverging results.
The aim of this PhD project is to establish the relationship between novel treatments/intervention outcomes and electrophysiological properties in humans, and identify the modes of action of these treatments in our brain, with the changes of these biomarkers before and after treatment. Ideally, the findings in this study can provide new insights and thus advance future “personalized” and “accurate” treatment strategies for MDD.