Learning from reward and prediction
|PhD ceremony:||A.J. (Hanneke) Geugies|
|When:||March 09, 2020|
|Supervisor:||prof. dr. R.A. (Robert) Schoevers|
|Co-supervisor:||dr. H.G. Ruhé|
|Where:||Academy building RUG|
|Faculty:||Medical Sciences / UMCG|
Treatment outcomes for major depressive disorder (MDD) need to be improved as both non-response rates after treatment and relapse/recurrence rates following remission are high. Ideally, early prediction of nonresponse and recurrence would either facilitate the choice, shortening the time to change to an adequate treatment or intensify treatment e.g. by combination of pharmacotherapy and psychotherapy. This dissertation has attempted to provide insight in mechanisms relevant for recurrence vulnerability and non-response to treatment. Moreover, we aimed to predict this non-response to treatment.
We conclude that acute MDD is characterized by impairments in reward response and reward connectivity and that temporal difference modeling of reward-related PE-signals give a more accurate representation of reward processing. During remission but still at high risk of recurrence, impairments in learning from rewarding and aversive events persist. These dysfunctions could represent trait rather than state-dependent abnormalities, and may be of importance for recurrence vulnerability. With regard to prediction of treatment outcome and non-response we confirmed that the MSM is a valid and reliable tool to predict poor outcome in MDD. When examining prediction of non-response with a more neurobiological, mechanistic approach, we provide evidence that differences in functional connectivity of the insula with the salience network is indicative for non-response which might encourage the choice for alternative treatment at an early stadium. These findings may contribute to enhanced understanding of pathophysiological mechanisms of (recurrent) MDD and prediction of non-response, ultimately improving the quality of life of people that suffer from MDD.