Ongoing projects

Within the RICH network we perform a lot of research. Our uniqueness lies with our interfaculty collaborations. Do you have an idea in mind that involves two or more faculties? Please let us know so we can facilitate starting your project and getting you in touch with our members.

Keep in mind that RICH started in 2020 and is still in development. There are some projects already set up or in progress. And of course you are always free to set up your own interfaculty collaboration. For inspiration, a few are listed below.

This list will be continuously updated.

Phase Transitions in Physics and Psychopathology

Summary: The purpose of this project is to study a tentative regime shift in momentary observations of daily experiences from a patient with major depressive disorder, using concepts from classical theory of phase transitions in physics. The research aim is twofold: (1) to use the patient’s momentary observation data to develop guidelines for the identification of an order parameter in a non-physical system, and (2) to assess the sensibility and usefulness of this translation of concepts between scientific disciplines. Part of this would be to examine how shared terminology surrounding critical transitions is used and understood on both ends of the spectrum, with physics on the one end and psychopathology on the other, and to take the first steps towards bridging the gap. In terms of methods, this implies a combination of literature review and data analysis supplemented by interviews with relevant researchers from the field.

Faculties involved: The project is formally supervised by a physicist from the High Field Magnet Laboratory (HFML) and a philosopher from the Institute for Science in Society (ISiS). Furthermore, the project will be carried out in collaboration with a behavioural scientist from the Behavioural Science Institute (BSI) and medical scientists from the Geriatrics department of the Radboud University Medical Center (Radboudumc).

Research (sub)questions:

To what extent can the concept of an order parameter, as known from physical theory of phase transitions, be applied to or identified in a non-physical system undergoing an apparent regime shift?
How are critical transitions understood in the scientific context of this dataset?
Which terminology is shared between this discipline and physics?
What degree of overlap exists between the usage of the terminology in these fields?
What are potential pitfalls or concepts susceptible to misunderstanding / mistranslation?
Which criteria could be used to help identify the order parameter of a particular non physical transition?
What does it mean if some but not all of the criteria are met?
What are the consequences if no order parameter can be found?

A paradigm shift? The need for novel research methods in medicine

Summary: Within healthcare, evidence-based medicine (EBM) dominates the field as the choice method to make decisions about individual patient care. The dominating research within EBM includes the use of randomized controlled trials (RCTs) and observational studies. With the rise of complexity in healthcare, there is an increasing need for research methods that tackle complexity due to the shift on patient and individual centered care. However, researchers may be unaware of alternative methods and how the use of EBM methods effects their methodological choice. Thus, this research proposes the use of Systems Dynamics (SD) to uncover the underlying structure and feedback loops that drive the methodological choice of healthcare researchers. SD is proposed as an appropriate method to investigate the processes which influence methodological choice due to the complex and dynamic nature of feedback loops which are involved in decision making. Feedback loops reveal processes in learning and decision making. By understanding why researchers make the choices they do, RUMC can better understand why novel research methods are or are not being used and if the status quo is adequate to answer the questions researchers are asking.

The goal of this research seeks to capture how researchers decide what methodology to choose and if these methodologies are found to be adequate. This will contribute practically to provide RUMC a springboard to developing the use of novel research methods and a broad inventory of which research problems and questions are lacking adequate research methods. This will also provide insights into how EBM takes shape while considering the contributions RUMC makes towards EBM.

Faculties involved: Etiënne Rouwette (Nijmegen School of Management), René Melis (Radboudumc) & Luca Consoli (Institute for Science in Society (ISiS))

Research (sub)questions:

What is the underlying structure that drive Radboud University medical center researcher’s methodological choice?
What problems do researchers currently experience when using their current methodology?
What are the feedback loops within the structure which keep researchers from implementing novel methods?

Multilayer network models of psychiatric disorders – A proof-of concept study

Summary: In recent years, there has been an increased recognition that psychiatric disorders are complex, multidimensional phenomena: they involve a complex interplay between the individual, their symptoms, physiology (e.g., their neurobiology), environmental and socio-cultural factors, amongst others. A potential way to do justice to the multidimensional nature of psychiatric disorders is to conceptualize them as multilayer networks, i.e., networks comprised of multiple layers with connections between and within the layers.

In this interdisciplinary proof-of-concept project, the following question will be addressed:

What are the methodological possibilities and challenges in estimating multidimensional multilayer networks of psychiatric disorders?

Faculties involved: Fred Hasselman/Jolien Venhuizen (BSI), Nina de Boer/Leon de Bruin (FFTR), Healthy Brain Study (as data source)

Research (sub)questions:

How should multiscale data be obtained and organized in order for a multilayer network model to be estimated?
What kind of topological properties can be estimated on the basis of a multilayer network model of psychiatric disorders?
How does the information a multilayer network provides, compare to the information provided by a network in which all dimensions are included in a monolayer network?
Can different dimensions and time scales be estimated simultaneously in one (3D) multilayer network model?
Are there alternative ways to estimate multidimensional models using other methods from the complexity science toolbox?
How does the estimation of individual network models compare to the estimation of group-level network models?
How to best visualize such a model?