Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

Precision psychiatry: predicting predictability.

Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.

The interacting partner as the immediate environment: Personality, interpersonal dynamics, and bodily synchronization.

OBJECTIVE: In social interactions, humans tend to naturally synchronize their body movements. We investigated interpersonal synchronization in conversations and examined its relationship with personality differences and post-interaction appraisals. METHOD: In a 15-minute semi-structured conversation, 56 previously-unfamiliar dyads introduced themselves, followed by self-disclosing and argumentative conversations. Their bodily movements were video-recorded in a standardized room (112 young adults, aged 18-33, mean = 20.54, SD = 2.74; 58% Dutch, 31% German, 11% other). Interpersonal bodily synchronization was estimated as (a) synchronization strength using Windowed Lagged Cross-Correlations and (b) Dynamic Organization (Determinism/Entropy/Laminarity/Mean Line) using Cross-Recurrence Quantification Analysis. Bodily synchronization was associated with differences in Agreeableness and Extraversion (IPIP-NEO-120) and post-conversational appraisals (affect/closeness/enjoyment) in mixed-effect models. RESULTS: Agreeable participants exhibited higher complexity in bodily synchronization dynamics (higher Entropy) than disagreeable individuals, who also reported more negative affect afterward. Interpersonal synchronization was stronger among extroverts than among introverts and extroverts appraised conversations as more positive and enjoyable. Bodily synchronization strength and dynamic organization were related to the type of conversation (self-disclosing/argumentative). CONCLUSIONS: Interpersonal dynamics were intimately connected to differences in Agreeableness and Extraversion, varied across situations, and these parameters affected how pleasant, close, and enjoyable each conversation felt.

From Metaphor to Computation: Constructing the Potential Landscape for Multivariate Psychological Formal Models.

For psychological formal models, the stability of different phases is an important property for understanding individual differences and change processes. Many researchers use landscapes as a metaphor to illustrate the concept of stability, but so far there is no method to quantify the stability of a system's phases. We here propose a method to construct the potential landscape for multivariate psychological models. This method is based on the generalized potential function defined by Wang et al. (2008) and Monte Carlo simulation. Based on potential landscapes we define three different types of stability for psychological phases: absolute stability, relative stability, and geometric stability. The panic disorder model by Robinaugh et al. (2019) is used as an example, to demonstrate how the method can be used to quantify the stability of states and phases, illustrate the influence of model parameters, and guide model modifications. An R package, simlandr, was developed to provide an implementation of the method.

Harvesting random embedding for high-frequency change-point detection in temporal complex systems.

Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and model-free method to achieve the mission based merely on the time-series data recorded from real-world systems. Here, we develop such a model-free approach, named temporal change-point detection (TCD), and integrate both dynamical and statistical methods to address this important challenge in a novel way. The proposed TCD approach, basing on exploitation of spatial information of the observed time series of high dimensions, is able not only to detect the separate change points of the concerned systems without knowing, a priori, any information of the equations of the systems, but also to harvest all the change points emergent in a relatively high-frequency manner, which cannot be directly achieved by using the existing methods and techniques. Practical effectiveness is comprehensively demonstrated using the data from the representative complex dynamics and real-world systems from biology to geology and even to social science.

Is Long COVID a State of Systemic Pericyte Disarray?

The most challenging aspect of Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) or Long COVID remains for the discordance between the viral damage from acute infection in the recent past and susceptibility of Long COVID without clear evidence of post infectious inflammation or autoimmune reactions. In this communication we propose that disarray of pericytes plays a central role in emerge of Long COVID. We assume pericytes are agents with "Triple-A" qualities, i.e., analyze-adapt and advance, necessary for sustainability of host homeostasis. Based on this view, we further suggest Long COVID may provide a model system to integrate system theory and complex adaptive systems to explore a new class of maladies those are currently not well defined and with no remedies.

A complex dynamical systems approach to the development of feeding problems in early childhood.

Though it is commonly agreed upon that the development of feeding problems in early childhood is a complex process, much of the research on these problems has a component-oriented focus, and very little attention is paid to the mechanisms that lead to these kinds of problems in individual children. The aim of this theoretical paper is to interpret the development of feeding problems in early childhood from a complex dynamical systems viewpoint. In addition to its focus on self-organization and nonlinearity, this approach defines several central properties of development: soft-assembly, embodiment, iterativity, the emergence of higher-order properties, and intra-individual variability. In this paper, I argue that each of these properties is highly relevant for understanding feeding problems and discuss the implications of this for both clinical practice and research purposes.

A theoretical and empirical modeling of anxiety integrated with RDoC and temporal dynamics.

The newly launched Research Domain Criteria (RDoC) emphasize specific mechanisms over diagnostic categories of psychopathology. In our view, RDoC provides a useful heuristic for mental health disorders, but does not capture the complexity of psychological data when proposed mechanisms are viewed as static entities. However, temporal and complex system dynamics may advance RDoC's utility. By investigating temporal patterns within trajectories and the interaction of complex networks, we propose that dynamic modeling provides comprehensive methods with which to investigate the etiopathology and maintenance of mental health disorders. We examine applications of dynamical systems to periphery physiology, an RDoC construct that has been widely used in psychological science. A review of the literature suggests methodological problems with aggregate and reductive models. We present a dynamical systems modeling of anxiety which suggests avenues for future biomarker research. This model appears congruent with RDoC and recent learning theory.

Socio-ecological dynamics and challenges to the governance of Neglected Tropical Disease control.

The current global attempts to control the so-called "Neglected Tropical Diseases (NTDs)" have the potential to significantly reduce the morbidity suffered by some of the world's poorest communities. However, the governance of these control programmes is driven by a managerial rationality that assumes predictability of proposed interventions, and which thus primarily seeks to improve the cost-effectiveness of implementation by measuring performance in terms of pre-determined outputs. Here, we argue that this approach has reinforced the narrow normal-science model for controlling parasitic diseases, and in doing so fails to address the complex dynamics, uncertainty and socio-ecological context-specificity that invariably underlie parasite transmission. We suggest that a new governance approach is required that draws on a combination of non-equilibrium thinking about the operation of complex, adaptive, systems from the natural sciences and constructivist social science perspectives that view the accumulation of scientific knowledge as contingent on historical interests and norms, if more effective control approaches sufficiently sensitive to local disease contexts are to be devised, applied and managed. At the core of this approach is an emphasis on the need for a process that assists with the inclusion of diverse perspectives, social learning and deliberation, and a reflexive approach to addressing system complexity and incertitude, while balancing this flexibility with stability-focused structures. We derive and discuss a possible governance framework and outline an organizational structure that could be used to effectively deal with the complexity of accomplishing global NTD control. We also point to examples of complexity-based management structures that have been used in parasite control previously, which could serve as practical templates for developing similar governance structures to better manage global NTD control. Our results hold important wider implications for global health policy aiming to effectively control and eradicate parasitic diseases across the world.

Understanding Policy Diffusion in the U.S.: An Information-Theoretical Approach to Unveil Connectivity Structures in Slowly Evolving Complex Systems.

Detecting and explaining the relationships among interacting components has long been a focal point of dynamical systems research. In this paper, we extend these types of data-driven analyses to the realm of public policy, whereby individual legislative entities interact to produce changes in their legal and political environments. We focus on the U.S. public health policy landscape, whose complexity determines our capacity as a society to effectively tackle pressing health issues. It has long been thought that some U.S. states innovate and enact new policies, while others mimic successful or competing states. However, the extent to which states learn from others, and the state characteristics that lead two states to influence one another, are not fully understood. Here, we propose a model-free, information-theoretical method to measure the existence and direction of influence of one state's policy or legal activity on others. Specifically, we tailor a popular notion of causality to handle the slow time-scale of policy adoption dynamics and unravel relationships among states from their recent law enactment histories. The method is validated using surrogate data generated from a new stochastic model of policy activity. Through the analysis of real data in alcohol, driving safety, and impaired driving policy, we provide evidence for the role of geography, political ideology, risk factors, and demographic and economic indicators on a state's tendency to learn from others when shaping its approach to public health regulation. Our method offers a new model-free approach to uncover interactions and establish cause-and-effect in slowly-evolving complex dynamical systems.

Improvisation and the self-organization of multiple musical bodies.

Understanding everyday behavior relies heavily upon understanding our ability to improvise, how we are able to continuously anticipate and adapt in order to coordinate with our environment and others. Here we consider the ability of musicians to improvise, where they must spontaneously coordinate their actions with co-performers in order to produce novel musical expressions. Investigations of this behavior have traditionally focused on describing the organization of cognitive structures. The focus, here, however, is on the ability of the time-evolving patterns of inter-musician movement coordination as revealed by the mathematical tools of complex dynamical systems to provide a new understanding of what potentiates the novelty of spontaneous musical action. We demonstrate this approach through the application of cross wavelet spectral analysis, which isolates the strength and patterning of the behavioral coordination that occurs between improvising musicians across a range of nested time-scales. Revealing the sophistication of the previously unexplored dynamics of movement coordination between improvising musicians is an important step toward understanding how creative musical expressions emerge from the spontaneous coordination of multiple musical bodies.