Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks

Abstract

The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order transitions in extremely noisy data. As a proof of principle, the present paper provides an example of a recurrence network based analysis strategy which can be implemented in a clinical setting in which data from an individual is continuously monitored for the purpose of making decisions about diagnosis and intervention. Specifically, it is demonstrated that measures based on the geometry of the phase space can serve as Early Warning Signals of imminent phase transitions. A publicly available multivariate time series is analyzed using so-called cumulative Recurrence Networks (cRN), which are recurrence networks with edges weighted by recurrence time and directed towards previously observed data points. The results are compared to previous analyses of the same data set, benefits, limitations and future directions of the analysis approach are discussed.

The best of both worlds? General principles of psychopathology in personalized assessment

“…Also, dynamic factor analysis could be examined in the present context to derive dimensions that can be interpreted as personalized latent variables which can then be studied to identify transitions (Fisher, 2015). Multidimensional recurrence quantification analysis could be used to study transitions in personalized self-ratings without any dimension reduction, thereby leading to even more fine-grained insights at the person level (Hasselman, 2022). Last, moving window PCA may be used to study changes over time in the PC1; increases over time in the explained variance of the PC1 can be early-warning signals for upcoming transitions, for which the item loadings and distribution of the data could reveal the direction of change (Lever et al, 2020;Schreuder et al, 2022).…”

“…For the classification of the change profiles, we first used the function ts_levels in the R-package casnet (Hasselman, 2022), which uses the function rpart from the R-package rpart (Therneau & Atkinson, 2022). We set the parameters minDataSplit and minLevelDuration to 7, meaning that stable levels should at least last one week.…”

Modeling psychopathology: 4D multiplexes to the rescue

“…However, it is worth mentioning that there is no agreed upon taxonomy of multiplexes; neither is there a clear consensus as to how multiplexes are distinct from other multi-layered networks (Kivelä et al, 2014). Some authors suppose, for instance, that multiplexes necessarily contain the same variables across layers or that all variables operate at the same temporal scales (see Hasselman, 2022). However, this does not match research practice (e.g., Boccaletti et al, 2014).…”

“…A notable exception is a recent publication byHasselman (2022). Hasselman suggests a six-layer (mood, physical, self-esteem, mental unrest, sleep quality and experience of the day) temporal multiplex that seeks to evaluate the coupling dynamics between these different variables across time scales to detect early warning signals of psychopathology.…”

Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development

“…Such research programs are based on a weak complexity commitment (cf. Hasselman, 2022), in which there is a consensus about the theoretical object of study being a complex dynamical system with many interacting components, without any profound consequences for modal research practices, such as measurement models, study design and data analytic techniques. Even if it is recognized that time series data of human behavior reveal all the hallmarks of complex dynamics, such as non-stationarity, heterogeneity, and long-range temporal correlations (Olthof et al, 2020), these properties are often considered a nuisance factor that should be removed, by design (e.g.…”