Recurrence plots and recurrence quantification analysis
http://www.recurrence-plot.tk/
β οΈ Some posts flutter over as cross-posts from Bluesky.
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Physiological aging is associated with progressive changes in brain function, including neural oscillations. Recurrence Quantification Analysis (RQA) may provide a nuanced perspective on neural dynamics in aging. Rosmarinic acid (RA) has shown promise in mitigating age-related neurodegeneration. Its effects on EEG complexity in aging models remain unexplored. This study aimed to investigate age-related changes in RQA metrics and to assess whether RA modulates these alterations in a rodent aging model. Aging was induced in female rats via D-galactose administration. RA was administered to aged model animals. Urethane-induced EEG were obtained and analyzed across delta and theta bands. RQA parametersβdeterminism (DET), entropy (ENTR), and laminarity (LAM)βwere computed for frontal and temporal regions. Aging was associated with a significant increase in DET and LAM, particularly in the temporal cortex, indicating enhanced regularity and persistence of EEG patterns. Concurrently, ENTR values declined, suggesting reduced signal complexity. RA partially reversed these trends, notably decreasing DET and LAM while increasing ENTR values in the temporal cortex. No significant changes were observed in the frontal cortex. This study underscores the utility of RQA in capturing nonlinear EEG alterations associated with aging and highlights RA as a promising compound for preserving cortical dynamics in senescence.
Northern Tibetan Plateau uplift and global cooling drove synchronous monsoon intensification and turnover of Asian mammal communities at ~8.7βMa, according to Asian monsoon reconstructions and mammalian fossil records plus model simulations.
Recurrence analysis allows the investigation of self-similarities in time series. Different degrees of regularity of behaviours, or different typologies of chaos, help characterise physical phenomena whose properties are expressed by time series. We consider here the special case of time series of human brain activity in the insula, an area particularly relevant for emotional and cognitive processing. Starting from time series obtained using functional magnetic resonance imaging, we adopt recurrence plots to investigate differences between normal and selected pathological behaviours. We also present a technique to encode time series into quantum-inspired states, by constructing a density matrix via a kernel mapping. Recurrence structures are derived from similarities between the components of its principal eigenvector. The obtained results highlight differences in behaviour between the time series. Overall, this conceptual study bridges ideas from nonlinear physics, quantum physics, and medical physics.
The paper explores the application of two nonlinear dynamics methods of phase dynamics modeling and the partial mean conditional probabilities of recurrence. The aim of the study is to identify triplewise couplings in three time series extracted from physiological rhythms of the respiratory, cardiovascular, and nervous systems. The determination reliability of three-oscillator network coupling structure was verified using the RΓΆssler model of three interacting oscillators with known unidirectional connections. Differences in coupling configurations between the respiratory rhythm oscillations, blood pressure variability, and variability of neuronal activity in medulla oblongata neurons were identified in datasets of two groups. The use of the nonlinear dynamics methods made it possible to determine the influence of a pathological condition on the triplewise interactions of multivariate time series extracted from physiological rhythms.
Trajectories of units moving on networks are relevant for nonlinear dynamical systems as diverse as polymers, ocean drifters, and human mobility. Although RQA is a well-researched tool with applications in many areas, it has rarely been used for spatial trajectories on networks. Here, we explore the use of RQA for paths on networks. We find that path dynamics on networks display recurrence patterns that are not often described in other applications of recurrence analysis. In particular, the combination of diagonal lines and perpendicular diagonal lines, indicates backtracking paths. We find that recurrence analysis for path dynamics on networks can be helpful to (a) better understand the network structure if dynamic and recurrence plots are known, (b) better understand the dynamics if network and recurrence plots are known, and (c) understand the interaction between path dynamics and the underlying network.
Radio refractivity chaos analyzed via #recurrence_quantification_analysis; humid stations showed higher complexity than semi-arid ones, affecting microwave link predictability seasonally
https://www.sciencedirect.com/science/article/pii/S0273117726001365