Recurrence Quantification for the Analysis of Coupled Processes in Aging

Brick, Timothy R.; Gray, Allison; Staples, Angela D.

doi:10.1093/geronb/gbx018

Cited by 17 publications

(11 citation statements)

References 56 publications

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“…For instance, the attractor of neuron models tend to show a tightly clustered region (representing no spiking) and a long-tailed or spiral structures indicative of spiking (e.g., Hindmarsh & Rose, 1984;Hodgkin & Huxley, 1952). For quantitative analysis of attractors, researchers may use analytic methods such as recurrence analysis, maximal Lyapunov exponent estimation, or measures of attractor similarity to obtain values quantifying aspects both within and between attractors (e.g., Brick, Gray, & Staples, 2018;Lefèvre, Lepresle, & Chariot, 2014;Peppoloni, Lawrence, Ruffaldi, & Valero-Cuevas, 2017;Timofejeva, Poskuviene, Cao, & Ragulskis, 2018).…”

Section: Havok Analysis Resultsmentioning

confidence: 99%

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

Moulder

Мартынова

Boker

2022

Multivariate Behavioral Research

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Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.

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Section: Havok Analysis Resultsmentioning

confidence: 99%

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

Moulder

Мартынова

Boker

2022

Multivariate Behavioral Research

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“…Each participant's trajectory was scaled to 2,500 sample points before the analysis. Although RQA can be computed for any length of data, Brick et al ( 2018 ) suggest a minimum of three or four measurements within each repetition of a pattern of interest. Our 100 Hz sampling rate would give 10 samples in a single cycle of a 10 Hz signal.…”

Section: Methodsmentioning

confidence: 99%

Recurrence Quantification Analysis of Ankle Kinematics During Gait in Individuals With Chronic Ankle Instability

Yen¹,

Qian²,

Folmar³

et al. 2022

Front. Sports Act. Living

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PurposeAn investigation of the ankle dynamics in a motor task may generate insights into the etiology of chronic ankle instability (CAI). This study presents a novel application of recurrence quantification analysis (RQA) to examine the ankle dynamics during walking. We hypothesized that CAI is associated with changes in the ankle dynamics as assessed by measures of determinism and laminarity using RQA.MethodsWe recorded and analyzed the ankle position trajectories in the frontal and sagittal planes from 12 participants with CAI and 12 healthy controls during treadmill walking. We used time-delay embedding to reconstruct the position trajectories to a phase space that represents the states of the ankle dynamics. Based on the phase space trajectory, a recurrence plot was constructed and two RQA variables, the percent determinism (%DET) and the percent laminarity (%LAM), were derived from the recurrence plot to quantify the ankle dynamics.ResultsIn the frontal plane, the %LAM in the CAI group was significantly lower than that in the control group (p < 0.05. effect size = 0.86). This indicated that the ankle dynamics in individuals with CAI is less likely to remain in the same state. No significant results were found in the %DET or in the sagittal plane.ConclusionA lower frontal-plane %LAM may reflect more frequent switching between different patterns of neuromuscular control states due to the instabilities associated with CAI. With further study and development, %LAM may have the potential to become a useful biomarker for CAI.

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“…Missing bins and irregular sampling rates are nonissues since every node is treated as a random sample from the underlying manifold: RNs have been used to analyse irregularly-sampled paleoclimate data (Donges et al, 2011a,b). This could potentially make recurrence networks extremely useful for analyzing behavioral data (particularly longitudinal studies) which is often irregularly sampled [a similar algorithm-Recurrence Quantification Analysis has been used in longitudinal behavioral data (Brick et al, 2017;Danvers et al, 2020)].…”

Section: Recurrence Networkmentioning

confidence: 99%

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

Varley

Sporns

2022

Front. Neurosci.

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In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as “network neuroscience.” In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, “intrinsic manifold” from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

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Recurrence Quantification for the Analysis of Coupled Processes in Aging

Cited by 17 publications

References 56 publications

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

Recurrence Quantification Analysis of Ankle Kinematics During Gait in Individuals With Chronic Ankle Instability

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

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