Dataset Information

Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) - A Tutorial in R.

ABSTRACT: This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a recognition that many behavioral and neurophysiological processes are intrinsically time dependent and reliant on environmental and social context has emerged. Recurrence-based methods are particularly suited for time-series that are non-stationary or have complicated dynamics, such as longer recordings of continuous physiological or movement data, but are also useful in the case of time-series of symbolic data, as in the case of text/verbal transcriptions or categorically coded behaviors. In the past, they have been used to assess changes in the dynamics of, or coupling between physiological and behavioral measures, for example in joint action research to determine the co-evolution of the behavior between individuals in dyads or groups, or for assessing the strength of coupling/correlation between two or more time-series. In this paper, we provide readers with a conceptual introduction, followed by a step-by-step explanation on how the analyses are performed in R with a summary of the current best practices of their application.

SUBMITTER: Wallot S

PROVIDER: S-EPMC6288366 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) - A Tutorial in R.

Wallot Sebastian S Leonardi Giuseppe G

Frontiers in psychology 20181204

This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a recognition that many behavioral and neurophysiological processes are intrinsically time dependent and reliant on environmental and social context has emerged. Recurrence-based methods a ...[more]

PMID: 30564161

Similar Datasets

Project description:ObjectivesTo investigate health literacy profiles using a multidimensional tool in the elderly, analyze the factors related to health literacy, and explore the relationships between health literacy and health-related behaviors.MethodsA cross-sectional survey including 440 participants was conducted at a community health center and a village health center in Changsha, Hunan Province, between June 2020 and August 2020. We used the Health Literacy Questionnaire (HLQ) to assess the elderly's health literacy. Sociodemographic data and health-related behaviors of them were surveyed with a self-designed questionnaire. Latent profile analysis, Pearson's chi-squared and ordinal logistic regression were used to analyze the data.ResultsThe median age of the 440 respondents was 68 years. The participants had the lowest scores in the "appraisal of health information" subscale (2.22 ± 0.52), followed by "navigating the healthcare system" subscale (2.89 ± 0.81) of HLQ. Based on the analysis of three profiles, respondents who were 60-74 years (OR = 2.06, 95% CI: 1.23-3.42, P = 0.006), living in urban areas (OR = 3.28, 95% CI: 2.17-4.94, P < 0.001), with secondary education or above (OR = 2.86, 95% CI: 1.92-4.27, P < 0.001), and having health insurance (OR = 1.89, 95% CI: 1.02-3.51, P = 0.042) were significantly associated with health literacy. Statistically significant associations were found between health literacy level and health-related behaviors, including medical service-seeking behavior (χ 2 = 25.14, P < 0.001), exercising regularly (χ 2 = 34.08, P < 0.001), and taking a medical examination in the past 12 months (χ 2 = 24.76, P < 0.001).ConclusionThe multidimensional health literacy survey has identified the low health literacy level among the elderly in community settings. It revealed the relationships of sociodemographic characteristics, including age, education level and residence, with health literacy. These findings emphasized the importance of health literacy in promoting health behaviors, guiding a profound understanding of the Chinese elderly's health needs and health literacy to develop community-based health promotion interventions.

Project description:Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Dataset Information

Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) - A Tutorial in R.

Publications

Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) - A Tutorial in R.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets