Periodic Reporting for period 3 - Psychosystems (Psychosystems:Consolidating Network Approaches to Psychopathology)
Reporting period: 2018-09-01 to 2020-02-29
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.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
The Psychosystems Project (www.psychosystems.org) has developed into a leading center of network theory and methodology as applied to psychological constructs. One important contribution to science and society that has been achieved in this project is the articulation of a novel theory of mental disorders, in which these result from interactions between symptoms rather than from a latent disorder. This theory is now guiding scientific research in various scientific groups around the world, and if accurate may have important ramifications for treatment. We have in addition worked on developing new methodologies to extract such symptom networks from various kinds of data and to analyze them mathematically. These methodologies have been implemented in freely available software modules, that are now being used by researchers from many scientific fields to chart network structures operative in psychopathology. In addition, several collaborations with leading research groups in psychiatry, clinical psychology, methodology, statistics, and dynamical systems theory have been set up. In these collaborations, researchers from these different fields work together to better understand specific mental disorders like Panic Disorder, Major Depression, and Schizophrenia, by representing them in terms of dynamical network models.
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
Both the theoretical work executed in this project and the statistical methods that arise from it extend beyond the current state of the art. For example, in the Psychosystems Project we have developed methods to estimate networks that change over time, methods to analyze networks consisting of mixed variables, and networks that at the same time represent dynamical processes and individual differences in these processes. None of these methods were available to researchers before this project started. In addition, the network theory formulated in this project is generating much new empirical research that approaches the problem of mental disorders from a new angle. These research projects are met with expectation, as they may reveal new insights into the dynamical structure of disorders, which may lead to novel approaches to treatment in turn.