Mapping the multicausality of Alzheimer’s disease through group model building

Uleman, Jeroen F.; Melis, René J. F.; Quax, Rick; Zee, Eddy A. van der; Thijssen, Dick; Dresler, Martin; Rest, Ondine van de; Velpen, Isabelle F. van der; Adams, Hieab H.H.; Schmand, Ben; Kok, Inge M.C.M. de; Bresser, Jeroen de; Richard, Edo; Verbeek, Marcel M.; Hoekstra, Alfons G.; Rouwette, E.A.J.A.; Rikkert, Marcel G M Olde

doi:10.1007/s11357-020-00228-7

Cited by 32 publications

(91 citation statements)

References 70 publications

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“…For example, together with the domain experts, the modeler can develop a model narrative that describes how variables influence one another over time, activating key feedback loops in the process (Kenzie et al, 2018). Additionally, although they do not consider the specific dynamics of the variables and causal links (Dablander & Hinne, 2019; van Elteren & Quax, 2019), network centrality measures could be applied to the aCLD to help identify potential leverage points (McGlashan et al, 2016; Uleman et al, 2021). Examples of such centrality measures that we have previously applied to a CLD are betweenness- and closeness centrality (Uleman et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…To facilitate this process, we suggest listing the experts and their domain(s) of expertise (e.g., Kenzie et al, 2018) in an expert-domain cross table, where every relevant domain is ideally represented by more than one expert to demonstrate the value of the support for the aCLD. The expert-domain cross table can be used to prove that both criteria (diversity and overlap) are sufficiently covered (Uleman et al, 2021).…”

Section: Documenting Authorshipmentioning

confidence: 99%

“…The information stored in today’s state-of-the-art CLD thus consists of variables, causal links in the form of arrows, and identified feedback loops (Bureš, 2017; Hovmand, 2013). A CLD can be used to unravel causal mechanisms, qualitatively interpret the phenomenon under study and/or conduct a network analysis (e.g., Uleman et al, 2021).…”

Section: Developing An Annotated Causal Loop Diagrammentioning

confidence: 99%

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Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Crielaard¹,

Uleman²,

Châtel³

et al. 2024

Psychological Methods

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Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate “what if” scenarios. We propose to realize this by deriving knowledge from experts’ mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM’s simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems.

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Section: Discussionmentioning

confidence: 99%

Section: Documenting Authorshipmentioning

confidence: 99%

Section: Developing An Annotated Causal Loop Diagrammentioning

confidence: 99%

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Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Crielaard¹,

Uleman²,

Châtel³

et al. 2024

Psychological Methods

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“…Although dementia is considered a multi-factorial condition, risk factors have mainly been researched in isolation. Only few maps and models on protective or risk factors of dementia are available [ 10 – 12 ]. The life-course model of Livingston, et al [ 10 ] shows potentially modifiable and non-modifiable risk factors of dementia.…”

Section: Introductionmentioning

confidence: 99%

Mapping the complexity of dementia: factors influencing cognitive function at the onset of dementia

et al. 2022

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Background Dementia is a multi-factorial condition rather than a natural and inevitable consequence of ageing. Some factors related to dementia have been studied much more extensively than others. To gain an overview of known or suspected influential factors is a prerequisite to design studies that aim to identify causal relationships and interactions between factors. This article aims to develop a visual model that a) identifies factors related to cognitive decline that signal the onset of dementia, b) structures them by different domains and c) reflects on and visualizes the possible causal links and interactions between these factors based on expert input using a causal loop diagram. Method We used a mixed-method, step-wise approach: 1. A systematic literature review on factors related to cognitive decline; 2. A group model building (GMB) workshop with experts from different disciplines; 3. Structured discussions within the group of researchers. The results were continuously synthesized and graphically transformed into a causal loop diagram. Results The causal loop diagram comprises 73 factors that were structured into six domains: physical (medical) factors (23), social health factors (21), psychological factors (14), environmental factors (5), demographic factors (5) and lifestyle factors (3). 57 factors were identified in the systematic literature review, additionally 16 factors, mostly of the social health cluster, were identified during the GMB session and the feedback rounds. Conclusion The causal loop diagram offers a comprehensive visualisation of factors related to cognitive decline and their interactions. It supports the generation of hypotheses on causal relationships and interactions of factors within and between domains.

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“…The resulting static viewpoint hinders our understanding of the complex, dynamic processes of the brain. The field of system dynamics (SD) has already begun to inform the biological sciences by facilitating the systemic study of complex diseases (Uleman et al ., 2020) and helping to improve patient treatment and outcomes (Rogers et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Insights from system dynamics that support experimental research: an exemplar of the NMDA receptor

Kelly

Rogers²,

Gallaher

2021

System Dynamics Review

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Mapping the multicausality of Alzheimer’s disease through group model building

Abstract: Alzheimer's disease (AD) is a complex, multicausal disorder involving several spatiotemporal scales and scientific domains. While many studies focus on specific parts of this system, the complexity Electronic supplementary material The online version of this article (

Cited by 32 publications

References 70 publications

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Mapping the complexity of dementia: factors influencing cognitive function at the onset of dementia

Insights from system dynamics that support experimental research: an exemplar of the NMDA receptor

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