Applying systems thinking to unravel the mechanisms underlying orthostatic hypotension related fall risk

Orthostatic hypotension (OH) is an established and common cardiovascular risk factor for falls. An in-depth understanding of the various interacting pathophysiological pathways contributing to OH-related falls is essential to guide improvements in diagnostic and treatment opportunities. We applied systems thinking to multidisciplinary map out causal mechanisms and risk factors. For this, we used group model building (GMB) to develop a causal loop diagram (CLD). The GMB was based on the input of experts from multiple domains related to OH and falls and all proposed mechanisms were supported by scientific literature. Our CLD is a conceptual representation of factors involved in OH-related falls, and their interrelatedness. Network analysis and feedback loops were applied to analyze and interpret the CLD, and quantitatively summarize the function and relative importance of the variables. Our CLD contains 50 variables distributed over three intrinsic domains (cerebral, cardiovascular, and musculoskeletal), and an extrinsic domain (e.g., medications). Between the variables, 181 connections and 65 feedback loops were identified. Decreased cerebral blood flow, low blood pressure, impaired baroreflex activity, and physical inactivity were identified as key factors involved in OH-related falls, based on their high centralities. Our CLD reflects the multifactorial pathophysiology of OH-related falls. It enables us to identify key elements, suggesting their potential for new diagnostic and treatment approaches in fall prevention. The interactive online CLD renders it suitable for both research and educational purposes and this CLD is the first step in the development of a computational model for simulating the effects of risk factors on falls

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

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 of AD is rarely studied as a whole. In this work, we apply systems thinking to map out known causal mechanisms and risk factors ranging from intracellular to psychosocial scales in sporadic AD. We report on the first systemic causal loop diagram (CLD) for AD, which is the result of an interdisciplinary group model building (GMB) process. The GMB was based on the input of experts from multiple domains and all proposed mechanisms were supported by scientific literature. The CLD elucidates interaction and feedback mechanisms that contribute to cognitive decline from midlife onward as described by the experts. As an immediate outcome, we observed several non-trivial reinforcing feedback loops involving factors at multiple spatial scales, which are rarely considered within the same theoretical framework. We also observed high centrality for modifiable risk factors such as social relationships and physical activity, which suggests they may be promising leverage points for interventions. This illustrates how a CLD from an interdisciplinary GMB process may lead to novel insights into complex disorders. Furthermore, the CLD is the first step in the development of a computational model for simulating the effects of risk factors on AD.Neuro Imaging Researc

Mobile Phones and Social Signal Processing for Analysis and Understanding of Dyadic Conversations

Social Signal Processing is the domain aimed at bridging the social intelligence gap between humans and machines via modeling, analysis and synthesis of nonverbal behavior in social interactions. One of the main challenges of the domain is to sense unobtrusively the behavior of social interaction participants, one of the key conditions to preserve the spontaneity and naturalness of the interactions under exam. In this respect, mobile devices offer a major opportunity because they are equipped with a wide array of sensors that, while capturing the behavior of their users with an unprecedented depth, are still invisible. This is particularly important because mobile devices are part of the everyday life of a large number of individuals and, hence, they can be used to investigate and sense natural and spontaneous scenarios

Reader Response: Biological Subtypes of Alzheimer Disease: A Systematic Review and Meta-analysis

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Complexer dan huisartsgeneeskunde kun je het niet krijgen

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An individualized systems model to optimize Alzheimer’s disease prevention strategies

Background: A large number of biopsychosocial factors are implicated in the prevention of Alzheimer’s Disease (AD). These factors are not independent causes but part of a complex causal network that underlies the condition. Computational models that would capture this system-wide multicausality could help identify causal pathways and inform multifactorial prevention strategies.Method: We developed a system dynamics model (SDM) from a causal loop diagram that was parameterized using empirical data from multiple cohorts (including the Alzheimer’s Disease Neuroimaging Initiative). The SDM contains over 20 known risk factors and pathophysiological processes, including blood pressure, smoking, neuronal dysfunction, and amyloid-beta and phosphorylated tau burden. We simulated 5-year cognitive decline trajectories for individuals and explored several “what if” scenarios regarding the effect of changes in modifiable risk factors on cognitive decline.Result: Our SDM was able to simulate the cognitive decline trajectories of individuals with good accuracy (< 20% mean absolute percentage error). These predictions also generalized well to an independent test sample from the same data set (<2% error increase). The effect of changes in modifiable risk factors on cognitive decline in the SDM were checked against literature reported ranges. We also developed a workflow to further calibrate and validate the SDM.Conclusion: Our SDM demonstrates the feasibility of system-wide modelling approaches for AD prevention. Such a simulation model could eventually be used to better understand the interactive effects of modifiable risk factors on AD pathophysiology and help optimize individualized prevention strategies