How can complexity modelling tools successfully be applied and explored to understand the onset and maintenance of common mental health disorders, like depression, in order to explore new targets for prevention and treatment?
Common mental health disorders (depressive- and anxiety disorders) are a worldwide epidemic and there is no evidence that the epidemic is subsiding. Depression is a major contributor to the overall global burden of disease (WHO). Globally, more than 300 million people suffer from depression. Psychological and pharmacological treatments are effective treatments, but only for half of treated patients. Further, relapse rates in depression after remission are unacceptably high. Despite a large body of research literature, effect sizes for treatment efficacy have not increased over at least four decades. Evidence for leading theories that explain the onset and maintenance of depression is fragmented. Depression is seen as a disorder that is caused by interplay of mental-, biological, stress related- and societal factors that can change over time, characterised by large individual differences. One of the main research challenges is to understand the causal interplay between these factors. An integrated approach is needed to generate insights for the development of innovative, more effective treatments.
In this IAS theme, we take a unique system dynamics view on common mental health disorders, examining all aspects from the molecular level all the way up to clinical practice. The theme includes various projects, focussing on different aspects of mental health.
Julia Anten, Student Assistant / Computational Science
Bas Chatel, Student Assistant / Computational Science
Ria Hoekstra, Research Assistant (FMG)
The above mentioned researchers and students, working on mental health disorders, often convene at the IAS on Wednesdays.