Recurrence Quantification Analysis

Webber, Charles L.; Marwan, Norbert

doi:10.1002/9780471740360.ebs1355

Cited by 85 publications

(72 citation statements)

References 65 publications

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“…The nonlinear analysis method used here, recurrence analysis (22), encompasses other quantifiers in addition to %R and %D, and other nonlinear processing methods have been described (27)(28)(29). The relative sensitivity and utility of other quantifiers and processing approaches have not been determined.…”

Section: Discussionmentioning

confidence: 99%

Simulated MR magnetic field induces steady‐state changes in brain dynamics: Implications for interpretation of functional MR studies

Marino

Carrubba

Frilot

et al. 2010

Magnetic Resonance in Med

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We examined whether a magnetic field comparable to one of the fields produced during MRI induced steady-state changes in brain electrical activity while the field was applied (called a presence effect to distinguish it from evoked potentials). The electroencephalogram was measured from standard scalp locations in the presence and absence of 100-200 mT, 60 Hz, and the effect of the field was evaluated by nonlinear (recurrence analysis) and linear techniques; individual subjects served as their own controls. Using recurrence analysis, changes in brain activity lasting 1 sec (the longest interval considered) were found in 21 of 22 subjects (P < 0.05 for each subject). The presence effect was not detected using linear analysis and was reversible, as indicated by a return of brain activity to baseline levels in all subjects within 2 sec of field offset. The possible role of artifacts or systematic errors was ruled out by studies using electrical phantoms and by analyses of electroencephalograms recorded during sham exposure. It is reasonable to expect that actual scanner magnetic fields also produce nonlinear steady-state perturbations of brain dynamical activity. The effect may influence the picture of brain connectivity inferred in some functional MR studies. Magn Reson Med 64:349-357, 2010. V C 2010 Wiley-Liss, Inc.Key words: nonlinear analysis; brain electrical activity; limitation on fMRI; electric fields; magnetic fields Cognitive activity is mediated by temporal-spatial interactions involving specialized regions of the brain (1). The interactions are commonly studied using functional MRI (fMRI) employing blood-oxygen-level-dependent (BOLD) contrast imaging (2).Averaged images obtained when subjects were and were not performing particular tasks are typically used to identify brain regions that mediated task-related cognitive processing (3,4). This strategy is based on some problematical assumptions and has neuropsychological drawbacks (5), but it affords good statistical power for detecting BOLD signals (6).In MRI, static (0 Hz) magnetic fields of 1-9 T align nuclear spins, high-frequency magnetic fields (10-400 MHz) of about 0.2 mT induce transitions between spin states, and low-frequency magnetic fields (100-1000 Hz) of about 50 mT produce imaging gradients (7); in fMRI, gradient parameters produce additional magnetic fields. Physical law and physiologic conditions in the brain (blood flow, tissue motion, cell movement, motion of the head) result in the simultaneous presence of electric fields (8).Human subjects can detect magnetic fields, as evidenced by their ability to trigger onset and offset evoked potentials (9). We were interested in whether, in addition to these transient changes in brain electrical activity, magnetic fields relevant to those used in MRI also produced continuous changes during the time the field was applied (''presence effect''). Depending on the dynamical nature of the effect (see below), such a finding would enhance the possibility that BOLD data obtained during functional imaging c...

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Section: Discussionmentioning

confidence: 99%

Simulated MR magnetic field induces steady‐state changes in brain dynamics: Implications for interpretation of functional MR studies

Marino

Carrubba

Frilot

et al. 2010

Magnetic Resonance in Med

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“…Zbilut & Webber 1992;Trulla et al 1996;Thiel et al 2004; The RQA also allows the study of other complexity indicators of dynamical systems, although these are not relevant to our aim (for a review see e.g. Zbilut & Webber 2006;Marwan et al 2007). In this work, we restricted our attention to DET and LAM.…”

Section: Methodsmentioning

confidence: 99%

Recurrence quantification analysis of two solar cycle indices

Stangalini¹,

Ermolli²,

Consolini³

et al. 2017

J. Space Weather Space Clim.

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Solar activity affects the whole heliosphere and near-Earth space environment. It has been reported in the literature that the mechanism responsible for the solar activity modulation behaves like a low-dimensional chaotic system. Studying these kind of physical systems and, in particular, their temporal evolution requires non-linear analysis methods. To this regard, in this work we apply the recurrence quantification analysis (RQA) to the study of two of the most commonly used solar cycle indicators; i.e. the series of the sunspot number (SSN), and the radio flux 10.7 cm, with the aim of identifying possible dynamical transitions in the system; a task which is particularly suited to the RQA. The outcome of this analysis reveals the presence of large fluctuations of two RQA measures: namely the determinism and the laminarity. In addition, large differences are also seen between the evolution of the RQA measures of the SSN and the radio flux. That suggests the presence of transitions in the dynamics underlying the solar activity. Besides it also shows and quantifies the different nature of these two solar indices. Furthermore, in order to check whether our results are affected by dataartefacts, we have also applied the RQA to both the recently recalibrated SSN series and the previous one, unveiling the main differences between the two data sets. The results are discussed in light of the recent literature on the subject.

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“…The backbone of CRA is the recurrence analysis that seeks regular patterns by comparing a single time series at different temporal scales. The basic idea of RA is to generate from the same time series, different time series that are temporally shifted trying to find the alignment across series that minimizes the distance between points (for a more detailed explanation refer to (Zbilut & Webber, 2006). CRA is an extended version of RA that simultaneously compares two different time series.…”

Section: Temporal Alignment: Crossrecurrence Analysismentioning

confidence: 99%

“…We can see, for example, that during the first 1000ms fixations of the two participants were similarly aligned on the same visual objects. Besides visualizing, it is also possible to quantify the percentage of recurrence to obtain a deeper understanding of the agreement between participants (Zbilut & Webber, 2006). However, this analysis applied to eye-tracking data presents several problems that seriously limit its applicability.…”

Section: Temporal Alignment: Crossrecurrence Analysismentioning

confidence: 99%

The statistical challenge of scan-path analysis

Coco

2009

2009 2nd Conference on Human System Interactions

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By knowing what people look at while performing tasks, we would be able to better design devices that facilitate users' interaction. Eye tracking is a useful technique to study visual attention but the data are often spatially and temporally complex to analyze. To this aim more sophisticated analytical tools are needed to give meaning to data, especially when both the spatial and temporal components are to be taken in synchrony. In this paper we review statistical methods for spatial and temporal analysis of eyemovement data pinning down advantages and disadvantages while proposing new directions of research.

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Recurrence Quantification Analysis

Cited by 85 publications

References 65 publications

Simulated MR magnetic field induces steady‐state changes in brain dynamics: Implications for interpretation of functional MR studies

Simulated MR magnetic field induces steady‐state changes in brain dynamics: Implications for interpretation of functional MR studies

Recurrence quantification analysis of two solar cycle indices

The statistical challenge of scan-path analysis

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