Mental disorders are a grave burden to individuals and to society. The aim of this project is to increase our understanding of mental disorders by developing network models for psychopathology. In network models, mental disorders arise through the (possibly reciprocal) interaction of symptoms (e.g. for the case of depression, insomnia->fatigue->concentration problems). The construction of such networks involves (a) the determination of their architecture (i.e. assessing which symptoms affect each other), and (b) the parameterization of their dynamics (i.e. assessing the functional form of the dynamic relations and the time scale at which symptoms affect each other). Over the past decade we have successfully developed methodologies suited to construct such models from various kinds of empirical data. The current proposal develops and consolidates this methodology. For instance, one particularly promising methodology consists in the estimation of person specific network structures on the basis of time series data. Using time series data, both the architecture and dynamical properties of symptomatology can be studied. For a long time, such data were scarce and it was almost impossible to adequately study the dynamics of disorders; at the present time, however, extensive time series are rapidly becoming available. This is due to the rise in popularity of Experience Sampling Methodology (ESM) and related electronic diary techniques. Using such methodology, researchers are able to gather time series of sufficient length for the implementation of time series modeling. This means that we can now construct methodology to determine the network dynamics of mental disorders at the level of the individual person. Once such networks have been established, we can analyze them by using methods taken from complex networks analysis and dynamical systems theory. This can help us understand why a person's symptomatology may rise to the level of disorder, which elements in that structure serve to perpetuate the disorder, and what the optimal treatment for an individual would be.
