Dynamic Multi-System Resilience in Human Aging/Developing dynamical indicators of resilience based on physiologic time series in older adult
November 12, 2018
2:20 pm - 3:00 pm
Sanne Gijzel (Radboud Univ.)
The NIA recently formulated a need to develop technically and clinically feasible method to assess systemic resilience as a predictor of individual recovery in older persons. Our group applies the generic dynamical systems theory to the aging human, which gives rise to new measures of resilience. A human being, like any complex system, is permanently subject to natural perturbations from the environment. When one continuously monitors physiologic parameters, the system’s dynamic responses to perturbations can be captured. A complex system with declining resilience shows slowing down of its dynamic responses. From time series of physiological parameters exhibiting a dynamic equilibrium, dynamical indicators of resilience (DIORs) can be calculated. A lack of resilience is reflected by an increase in three DIORs: the variance, temporal autocorrelation (states becoming more correlated with states on subsequent moments), and cross-correlations between the observed fluctuations. Importantly, as DIORs tap into the dynamic behavior rather than the mean state of systems, they may be more sensitive to discriminate subtle differences in the human capacity to resist and recover from health challenges than traditional health risk indicators.This talk will outline the current state-of-the-art of the development of DIORs in aging research. Challenges in the collection, analysis, and interpretation of physiologic time series data will be outlined. By highlighting applications of DIORs in related research fields like psychology and veterinary science, potential new research leads will be formulated.
Sanne Gijzel (Radboud Univ.) Link to the source page
During the first day, although the talks were very different, they very nicely complemented each other. It is amazing to see that we are all adopting slightly different approaches to investigating resilience in human aging but that they can all be placed in the larger framework of resilience of complex dynamical systems. We are all pioneering in this area and only sharing our struggles and recent insights was already very valuable, at least in my experience.
During the second day, I began seeing that we are working along 2 parallel lines:
- Finding ways to quantify resilience / resiliencies
- Increasing our understanding of the dynamics of the complex system in terms of resilience
- I liked Alfons' idea of making real-life examples of "Resilience is ........" in the form of a short narrative / artwork / graphical illustration / equations. I agree that these can be very helpful to increase our understanding of resilience and involve more people (e.g. clinicians) in the resilience thinking and discourse.
- Marcel commented that for humans, it's clear that there are alternative stable states in health, but we do not know what are the precise perturbations and positive feedbacks causing the transition. To increase our understanding about this, I think we need to start with making mechanistic models. We can use these mechanistic models to generate new hypotheses.
- Ingrid pointed out the difference between acute stressors (perturbations, e.g. a stimulus-response test) and slow stressors (drivers, e.g. aging).
|Title||Author name||Source name||Year||Citation count From Scopus. Refreshed every 5 days.||Page views||Related file|
|Early-warning signals for critical transitions||Marten Scheffer, Jordi Bascompte, William A. Brock, Victor Brovkin, Stephen R. Carpenter, Vasilis Dakos, Hermann Held, Egbert H. Van Nes, Max Rietkerk, George Sugihara||Nature||2009||2,153||30|
|Dynamical Resilience Indicators in Time Series of Self-Rated Health Correspond to Frailty Levels in Older Adults||Sanne M.W. Gijzel, Ingrid A. Van De Leemput, Marten Scheffer, Mattia Roppolo, Marcel G.M. Olde Rikkert, René J.F. Melis||Journals of Gerontology - Series A Biological Sciences and Medical Sciences||2017||30||7|
|Critical slowing down as early warning for the onset and termination of depression3||0||12|