1
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Junqueira Saldanha MH, Hirata Y. Solar activity facilitates daily forecasts of large earthquakes. CHAOS (WOODBURY, N.Y.) 2022; 32:061107. [PMID: 35778123 DOI: 10.1063/5.0096150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Despite the extensive literature related to earthquakes, an effective method to forecast and avoid occasional seismic hazards that cause substantial damage is lacking. The Sun has recently been identified as a potential precursor to earthquakes, although no causal relationship between its activity and the Earth's seismicity has been established. This study was aimed at investigating whether such a relationship exists and whether it can be used to improve earthquake forecasting. The edit distances between earthquake point processes were combined with delay-coordinate distances for sunspot numbers. The comparison of these two indicated the existence of unidirectional causal coupling from solar activity to seismicity on Earth, and a radial basis function regressor showed accuracy improvements in the largest magnitude prediction of next days by 2.6%-17.9% in the odds ratio when sunspot distances were included.
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Affiliation(s)
- Matheus Henrique Junqueira Saldanha
- Degree Programs in Systems and Information Engineering, Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
| | - Yoshito Hirata
- Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
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2
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Fast and effective pseudo transfer entropy for bivariate data-driven causal inference. Sci Rep 2021; 11:8423. [PMID: 33875707 PMCID: PMC8055902 DOI: 10.1038/s41598-021-87818-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/30/2021] [Indexed: 11/08/2022] Open
Abstract
Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters limit their applicability. Here we propose a computationally efficient measure for causality testing, which we refer to as pseudo transfer entropy (pTE), that we derive from the standard definition of transfer entropy (TE) by using a Gaussian approximation. We demonstrate the power of the pTE measure on simulated and on real-world data. In all cases we find that pTE returns results that are very similar to those returned by Granger causality (GC). Importantly, for short time series, pTE combined with time-shifted (T-S) surrogates for significance testing strongly reduces the computational cost with respect to the widely used iterative amplitude adjusted Fourier transform (IAAFT) surrogate testing. For example, for time series of 100 data points, pTE and T-S reduce the computational time by \documentclass[12pt]{minimal}
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\begin{document}$$82\%$$\end{document}82% with respect to GC and IAAFT. We also show that pTE is robust against observational noise. Therefore, we argue that the causal inference approach proposed here will be extremely valuable when causality networks need to be inferred from the analysis of a large number of short time series.
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3
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Satake A, Leong Yao T, Kosugi Y, Chen Y. Testing the environmental prediction hypothesis for community‐wide mass flowering in South‐East Asia. Biotropica 2021. [DOI: 10.1111/btp.12903] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Akiko Satake
- Department of Biology Faculty of Science Kyushu University Fukuoka Japan
| | | | - Yoshiko Kosugi
- Graduate School of Agriculture Kyoto University Kyoto Japan
| | - Yu‐Yun Chen
- Department of Natural Resources and Environmental Studies National Dong Hwa University Hualien Taiwan
- Center for Interdisciplinary Research on Ecology and Sustainability National Dong Hwa University Hualien Taiwan
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4
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Lehnertz K, Bröhl T, Rings T. The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges. Front Physiol 2020; 11:598694. [PMID: 33408639 PMCID: PMC7779628 DOI: 10.3389/fphys.2020.598694] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 11/30/2020] [Indexed: 12/30/2022] Open
Abstract
The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions-namely strength, direction, and functional form-from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
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5
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Partial cross mapping eliminates indirect causal influences. Nat Commun 2020; 11:2632. [PMID: 32457301 PMCID: PMC7251131 DOI: 10.1038/s41467-020-16238-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/22/2020] [Indexed: 12/27/2022] Open
Abstract
Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data. It is crucial yet challenging to identify cause-consequence relation in complex dynamical systems where direct causal links can mix with indirect ones. Leng et al. propose a data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world data.
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6
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Runge J, Nowack P, Kretschmer M, Flaxman S, Sejdinovic D. Detecting and quantifying causal associations in large nonlinear time series datasets. SCIENCE ADVANCES 2019; 5:eaau4996. [PMID: 31807692 PMCID: PMC6881151 DOI: 10.1126/sciadv.aau4996] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 09/17/2019] [Indexed: 05/07/2023]
Abstract
Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.
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Affiliation(s)
- Jakob Runge
- German Aerospace Center, Institute of Data Science, 07745 Jena, Germany
- Grantham Institute, Imperial College, London SW7 2AZ, UK
- Corresponding author.
| | - Peer Nowack
- Grantham Institute, Imperial College, London SW7 2AZ, UK
- Department of Physics, Blackett Laboratory, Imperial College, London SW7 2AZ, UK
- Data Science Institute, Imperial College, London SW7 2AZ, UK
| | | | - Seth Flaxman
- Data Science Institute, Imperial College, London SW7 2AZ, UK
- Department of Mathematics, Imperial College, London SW7 2AZ, UK
| | - Dino Sejdinovic
- The Alan Turing Institute for Data Science, London NW1 3DB, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
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7
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Lainscsek C, Gonzalez CE, Sampson AL, Cash SS, Sejnowski TJ. Causality detection in cortical seizure dynamics using cross-dynamical delay differential analysis. CHAOS (WOODBURY, N.Y.) 2019; 29:101103. [PMID: 31675829 PMCID: PMC6783296 DOI: 10.1063/1.5126125] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 09/17/2019] [Indexed: 05/14/2023]
Abstract
Most natural systems, including the brain, are highly nonlinear and complex, and determining information flow among the components that make up these dynamic systems is challenging. One such example is identifying abnormal causal interactions among different brain areas that give rise to epileptic activities. Here, we introduce cross-dynamical delay differential analysis, an extension of delay differential analysis, as a tool to establish causal relationships from time series signals. Our method can infer causality from short time series signals as well as in the presence of noise. Furthermore, we can determine the onset of generalized synchronization directly from time series data, without having to consult the underlying equations. We first validate our method on simulated datasets from coupled dynamical systems and apply the method to intracranial electroencephalography data obtained from epilepsy patients to better characterize large-scale information flow during epilepsy.
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Affiliation(s)
- Claudia Lainscsek
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA
| | - Christopher E Gonzalez
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA
| | - Aaron L Sampson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA
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8
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Leng S, Xu Z, Ma H. Reconstructing directional causal networks with random forest: Causality meeting machine learning. CHAOS (WOODBURY, N.Y.) 2019; 29:093130. [PMID: 31575149 DOI: 10.1063/1.5120778] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Accepted: 09/08/2019] [Indexed: 06/10/2023]
Abstract
Inspired by the decision tree algorithm in machine learning, a novel causal network reconstruction framework is proposed with the name Importance Causal Analysis (ICA). The ICA framework is designed in a network level and fills the gap between traditional mutual causality detection methods and the reconstruction of causal networks. The potential of the method to identify the true causal relations in complex networks is validated by both benchmark systems and real-world data sets.
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Affiliation(s)
- Siyang Leng
- School of Mathematical Sciences, Fudan University, Shanghai 200433, China
| | - Ziwei Xu
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
| | - Huanfei Ma
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
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9
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Surrogate Data Preserving All the Properties of Ordinal Patterns up to a Certain Length. ENTROPY 2019; 21:e21070713. [PMID: 33267427 PMCID: PMC7515228 DOI: 10.3390/e21070713] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 07/10/2019] [Accepted: 07/19/2019] [Indexed: 11/24/2022]
Abstract
We propose a method for generating surrogate data that preserves all the properties of ordinal patterns up to a certain length, such as the numbers of allowed/forbidden ordinal patterns and transition likelihoods from ordinal patterns into others. The null hypothesis is that the details of the underlying dynamics do not matter beyond the refinements of ordinal patterns finer than a predefined length. The proposed surrogate data help construct a test of determinism that is free from the common linearity assumption for a null-hypothesis.
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10
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Runge J, Bathiany S, Bollt E, Camps-Valls G, Coumou D, Deyle E, Glymour C, Kretschmer M, Mahecha MD, Muñoz-Marí J, van Nes EH, Peters J, Quax R, Reichstein M, Scheffer M, Schölkopf B, Spirtes P, Sugihara G, Sun J, Zhang K, Zscheischler J. Inferring causation from time series in Earth system sciences. Nat Commun 2019; 10:2553. [PMID: 31201306 PMCID: PMC6572812 DOI: 10.1038/s41467-019-10105-3] [Citation(s) in RCA: 137] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 04/17/2019] [Indexed: 11/25/2022] Open
Abstract
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
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Affiliation(s)
- Jakob Runge
- German Aerospace Center, Institute of Data Science, Mälzer Str. 3, 07745, Jena, Germany.
- Grantham Institute, Imperial College, London, SW7 2AZ, UK.
| | - Sebastian Bathiany
- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht, Fischertwiete 1, 20095, Hamburg, Germany
- Department of Environmental Sciences, Wageningen University, P.O. Box 47, NL-6700 AA, Wageningen, The Netherlands
| | - Erik Bollt
- Department of Mathematics, Clarkson Center for Complex Systems Science (C3S2), Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699-5815, USA
| | - Gustau Camps-Valls
- Image Processing Laboratory, Universitat de València, ES-46980, Paterna (València), Spain
| | - Dim Coumou
- Department of Water and Climate Risk, Institute for Environmental Studies (IVM), VU University Amsterdam, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands
- Potsdam Institute for Climate Impact Research, Earth System Analysis, Telegraphenberg A62, 14473, Potsdam, Germany
| | - Ethan Deyle
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Clark Glymour
- Department of Philosophy, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Marlene Kretschmer
- Potsdam Institute for Climate Impact Research, Earth System Analysis, Telegraphenberg A62, 14473, Potsdam, Germany
| | - Miguel D Mahecha
- Max Planck Institute for Biogeochemistry, PO Box 100164, 07701, Jena, Germany
| | - Jordi Muñoz-Marí
- Image Processing Laboratory, Universitat de València, ES-46980, Paterna (València), Spain
| | - Egbert H van Nes
- Department of Environmental Sciences, Wageningen University, P.O. Box 47, NL-6700 AA, Wageningen, The Netherlands
| | - Jonas Peters
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100, København, Denmark
| | - Rick Quax
- Institute for Informatics, University of Amsterdam, PO Box 94323, 1090 GH, Amsterdam, The Netherlands
- Institute of Advanced Studies, University of Amsterdam, Oude Turfmarkt 147, 1012, GC, Amsterdam, The Netherlands
| | - Markus Reichstein
- Max Planck Institute for Biogeochemistry, PO Box 100164, 07701, Jena, Germany
| | - Marten Scheffer
- Department of Environmental Sciences, Wageningen University, P.O. Box 47, NL-6700 AA, Wageningen, The Netherlands
| | - Bernhard Schölkopf
- Max Planck Institute for Intelligent Systems, Max Planck Ring 4, 72076, Tübingen, Germany
| | - Peter Spirtes
- Department of Philosophy, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - George Sugihara
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Jie Sun
- Department of Mathematics, Clarkson Center for Complex Systems Science (C3S2), Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699-5815, USA
- Department of Physics and Department of Computer Science, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699-5815, USA
| | - Kun Zhang
- Department of Philosophy, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Jakob Zscheischler
- Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092, Zurich, Switzerland
- Climate and Environmental Physics, University of Bern, Sidlerstrasse 5, 3012, Bern, Switzerland
- Oeschger Centre for Climate Change Research, University of Bern, Bern, 3012, Switzerland
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11
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Fonseca A, Kerick S, King JT, Lin CT, Jung TP. Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data. Front Hum Neurosci 2018; 12:418. [PMID: 30483080 PMCID: PMC6240698 DOI: 10.3389/fnhum.2018.00418] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 09/27/2018] [Indexed: 11/13/2022] Open
Abstract
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
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Affiliation(s)
- André Fonseca
- Center of Mathematics, Computation and Cognition, Federal University of ABC, São Paulo, Brazil.,Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - Scott Kerick
- US Army Research Laboratory, Aberdeen, MD, United States
| | - Jung-Tai King
- Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan
| | - Chin-Teng Lin
- Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia
| | - Tzyy-Ping Jung
- Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States
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12
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Beauchene C, Roy S, Moran R, Leonessa A, Abaid N. Comparing brain connectivity metrics: a didactic tutorial with a toy model and experimental data. J Neural Eng 2018; 15:056031. [PMID: 30095079 DOI: 10.1088/1741-2552/aad96e] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The objective of this paper is to didactically compare resting state connectivity networks computed using two different methods called phase locking value (PLV) and convergent cross-mapping (CCM). PLV is a ubiquitous measure of connectivity in electrophysiological research but is less often applied to fMRI BOLD timeseries since this model-based metric assumes that oscillatory coupling is a sufficient condition for connectivity. Alternatively, CCM is a model-free method, which detects potentially nonlinear causal influences based on the ability to estimate one timeseries with another and does not assume an oscillatory structure. APPROACH We use a toy dataset to test the PLV and CCM algorithms under different known synchronization conditions. Additionally, experimental resting state EEG and fMRI datasets are used for comparison. MAIN RESULTS The results show that the resting state brain networks computed using both algorithms produce similar results for both resting state EEG and fMRI datasets. For both neuroimaging datasets, the network characteristics follow the same trends and the similarity between the computed networks, for both algorithms, is highly significant. SIGNIFICANCE CCM is able to identify low or one-way connection strengths better than PLV but takes exponentially longer to compute. Based on these results, PLV provides a good metric for on-line network identification because it is both computationally fast and an excellent approximation of the network computed with CCM.
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Affiliation(s)
- Christine Beauchene
- Department of Mechanical Engineering, Center for Dynamic Systems Modeling and Control, Virginia Tech, Blacksburg, VA, United States of America
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13
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Vieira VJD, Costa SC, Correia SLN, Lopes LW, Costa WCDA, de Assis FM. Exploiting nonlinearity of the speech production system for voice disorder assessment by recurrence quantification analysis. CHAOS (WOODBURY, N.Y.) 2018; 28:085709. [PMID: 30180621 DOI: 10.1063/1.5024948] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 07/27/2018] [Indexed: 06/08/2023]
Abstract
This work summarizes the research related to digital speech signal processing with recurrence quantification analysis (RQA) applied to voice disorder assessment. The main motivation for these studies is the fact that RQA is able to exploit the nonlinear dynamical nature of the speech production system. Due to the use of recurrence quantification measures to represent the behavior of speech signals, promising results were obtained in the characterization and classification of laryngeal pathologies and voice disorders. These contributions may help one to evaluate the usability and efficiency of RQA in vocal disorder assessment.
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Affiliation(s)
- Vinícius J D Vieira
- Linguistics Graduate Program (PROLING), Federal University of Paraíba, Cidade Universitária, Campus I - Castelo Branco, João Pessoa, Paraíba 58051-900, Brazil
| | - Silvana C Costa
- Academic Unity of Industry, Federal Institute of Paraíba, Avenida Primeiro de Maio, 720 - Jaguaribe, João Pessoa, Paraíba 58015-435, Brazil
| | - Suzete L N Correia
- Academic Unity of Industry, Federal Institute of Paraíba, Avenida Primeiro de Maio, 720 - Jaguaribe, João Pessoa, Paraíba 58015-435, Brazil
| | - Leonardo W Lopes
- Speech and Language Pathology Department, Federal University of Paraíba, Cidade Universitária, Campus I - Castelo Branco, João Pessoa, Paraíba 58051-900, Brazil
| | - Washington C de A Costa
- Academic Unity of Industry, Federal Institute of Paraíba, Avenida Primeiro de Maio, 720 - Jaguaribe, João Pessoa, Paraíba 58015-435, Brazil
| | - Francisco M de Assis
- Electrical Engineering Department, Federal University of Campina Grande, Rua Aprígio Veloso, 882 - Universitário, Campina Grande, Paraíba 58429-900, Brazil
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14
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Amigó JM, Hirata Y. Detecting directional couplings from multivariate flows by the joint distance distribution. CHAOS (WOODBURY, N.Y.) 2018; 28:075302. [PMID: 30070509 DOI: 10.1063/1.5010779] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The identification of directional couplings (or drive-response relationships) in the analysis of interacting nonlinear systems is an important piece of information to understand their dynamics. This task is especially challenging when the analyst's knowledge of the systems reduces virtually to time series of observations. Spurred by the success of Granger causality in econometrics, the study of cause-effect relationships (not to be confounded with statistical correlations) was extended to other fields, thus favoring the introduction of further tools such as transfer entropy. Currently, the research on old and new causality tools along with their pitfalls and applications in ever more general situations is going through a time of much activity. In this paper, we re-examine the method of the joint distance distribution to detect directional couplings between two multivariate flows. This method is based on the forced Takens theorem, and, more specifically, it exploits the existence of a continuous mapping from the reconstructed attractor of the response system to the reconstructed attractor of the driving system, an approach that is increasingly drawing the attention of the data analysts. The numerical results with Lorenz and Rössler oscillators in three different interaction networks (including hidden common drivers) are quite satisfactory, except when phase synchronization sets in. They also show that the method of the joint distance distribution outperforms the lowest dimensional transfer entropy in the cases considered. The robustness of the results to the sampling interval, time series length, observational noise, and metric is analyzed too.
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Affiliation(s)
- José M Amigó
- Centro de Investigación Operativa, Universidad Miguel Hernández, Avda. de la Universidad s/n, 03202 Elche, Spain
| | - Yoshito Hirata
- Mathematics and Informatics Center, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan and The Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
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15
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Roy S, Jantzen B. Detecting causality using symmetry transformations. CHAOS (WOODBURY, N.Y.) 2018; 28:075305. [PMID: 30070527 DOI: 10.1063/1.5018101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Detecting causality between variables in a time series is a challenge, particularly when the relationship is nonlinear and the dataset is noisy. Here, we present a novel tool for detecting causality that leverages the properties of symmetry transformations. The aim is to develop an algorithm with the potential to detect both unidirectional and bidirectional coupling for nonlinear systems in the presence of significant sampling noise. Most of the existing tools for detecting causality can make determinations of directionality, but those determinations are relatively fragile in the presence of noise. The novel algorithm developed in the present study is robust and very conservative in that it reliably detects causal structure with a very low rate of error even in the presence of high sampling noise. We demonstrate the performance of our algorithm and compare it with two popular model-free methods, namely transfer entropy and convergent cross map. This first implementation of the method of symmetry transformations is limited in that it applies only to first-order autonomous systems.
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Bollt EM, Sun J, Runge J. Introduction to Focus Issue: Causation inference and information flow in dynamical systems: Theory and applications. CHAOS (WOODBURY, N.Y.) 2018; 28:075201. [PMID: 30070534 DOI: 10.1063/1.5046848] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Questions of causation are foundational across science and often relate further to problems of control, policy decisions, and forecasts. In nonlinear dynamics and complex systems science, causation inference and information flow are closely related concepts, whereby "information" or knowledge of certain states can be thought of as coupling influence onto the future states of other processes in a complex system. While causation inference and information flow are by now classical topics, incorporating methods from statistics and time series analysis, information theory, dynamical systems, and statistical mechanics, to name a few, there remain important advancements in continuing to strengthen the theory, and pushing the context of applications, especially with the ever-increasing abundance of data collected across many fields and systems. This Focus Issue considers different aspects of these questions, both in terms of founding theory and several topical applications.
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Affiliation(s)
- Erik M Bollt
- Clarkson Center for Complex Systems Science (C3S2), Clarkson University, Potsdam, New York 13699, USA
| | - Jie Sun
- Clarkson Center for Complex Systems Science (C3S2), Clarkson University, Potsdam, New York 13699, USA
| | - Jakob Runge
- German Aerospace Center (DLR), Institute of Data Science, Maelzerstrasse 3, 07745 Jena, Germany
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Runge J. Causal network reconstruction from time series: From theoretical assumptions to practical estimation. CHAOS (WOODBURY, N.Y.) 2018; 28:075310. [PMID: 30070533 DOI: 10.1063/1.5025050] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 05/25/2018] [Indexed: 06/08/2023]
Abstract
Causal network reconstruction from time series is an emerging topic in many fields of science. Beyond inferring directionality between two time series, the goal of causal network reconstruction or causal discovery is to distinguish direct from indirect dependencies and common drivers among multiple time series. Here, the problem of inferring causal networks including time lags from multivariate time series is recapitulated from the underlying causal assumptions to practical estimation problems. Each aspect is illustrated with simple examples including unobserved variables, sampling issues, determinism, stationarity, nonlinearity, measurement error, and significance testing. The effects of dynamical noise, autocorrelation, and high dimensionality are highlighted in comparison studies of common causal reconstruction methods. Finally, method performance evaluation approaches and criteria are suggested. The article is intended to briefly review and accessibly illustrate the foundations and practical problems of time series-based causal discovery and stimulate further methodological developments.
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Affiliation(s)
- J Runge
- German Aerospace Center, Institute of Data Science, Jena 07745, Germany
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Hirata Y, Aihara K. Dimensionless embedding for nonlinear time series analysis. Phys Rev E 2017; 96:032219. [PMID: 29347024 DOI: 10.1103/physreve.96.032219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Indexed: 06/07/2023]
Abstract
Recently, infinite-dimensional delay coordinates (InDDeCs) have been proposed for predicting high-dimensional dynamics instead of conventional delay coordinates. Although InDDeCs can realize faster computation and more accurate short-term prediction, it is still not well-known whether InDDeCs can be used in other applications of nonlinear time series analysis in which reconstruction is needed for the underlying dynamics from a scalar time series generated from a dynamical system. Here, we give theoretical support for justifying the use of InDDeCs and provide numerical examples to show that InDDeCs can be used for various applications for obtaining the recurrence plots, correlation dimensions, and maximal Lyapunov exponents, as well as testing directional couplings and extracting slow-driving forces. We demonstrate performance of the InDDeCs using the weather data. Thus, InDDeCs can eventually realize "dimensionless embedding" while we enjoy faster and more reliable computations.
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Affiliation(s)
- Yoshito Hirata
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Kazuyuki Aihara
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
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Assessment of resampling methods for causality testing: A note on the US inflation behavior. PLoS One 2017; 12:e0180852. [PMID: 28708870 PMCID: PMC5510825 DOI: 10.1371/journal.pone.0180852] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 06/06/2017] [Indexed: 01/21/2023] Open
Abstract
Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms.
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Amigó JM, Monetti R, Graff B, Graff G. Computing algebraic transfer entropy and coupling directions via transcripts. CHAOS (WOODBURY, N.Y.) 2016; 26:113115. [PMID: 27908002 DOI: 10.1063/1.4967803] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Most random processes studied in nonlinear time series analysis take values on sets endowed with a group structure, e.g., the real and rational numbers, and the integers. This fact allows to associate with each pair of group elements a third element, called their transcript, which is defined as the product of the second element in the pair times the first one. The transfer entropy of two such processes is called algebraic transfer entropy. It measures the information transferred between two coupled processes whose values belong to a group. In this paper, we show that, subject to one constraint, the algebraic transfer entropy matches the (in general, conditional) mutual information of certain transcripts with one variable less. This property has interesting practical applications, especially to the analysis of short time series. We also derive weak conditions for the 3-dimensional algebraic transfer entropy to yield the same coupling direction as the corresponding mutual information of transcripts. A related issue concerns the use of mutual information of transcripts to determine coupling directions in cases where the conditions just mentioned are not fulfilled. We checked the latter possibility in the lowest dimensional case with numerical simulations and cardiovascular data, and obtained positive results.
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Affiliation(s)
- José M Amigó
- Centro de Investigación Operativa, Universidad Miguel Hernández, 03202 Elche, Spain
| | | | - Beata Graff
- Department of Hypertension and Diabetology, Medical University of Gdansk, 80-952 Gdansk, Poland
| | - Grzegorz Graff
- Faculty of Applied Physics and Mathematics, Gdansk University of Technology, 80-233 Gdansk, Poland
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Correction: Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples. PLoS One 2016; 11:e0160864. [PMID: 27486999 PMCID: PMC4972412 DOI: 10.1371/journal.pone.0160864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
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