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A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression
Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate tim...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4905976/ https://www.ncbi.nlm.nih.gov/pubmed/27378901 http://dx.doi.org/10.3389/fninf.2016.00019 |
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author | Nicolaou, Nicoletta Constandinou, Timothy G. |
author_facet | Nicolaou, Nicoletta Constandinou, Timothy G. |
author_sort | Nicolaou, Nicoletta |
collection | PubMed |
description | Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C(NPMR), Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C(NPMR) on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C(NPMR) correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C(NPMR) is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications. |
format | Online Article Text |
id | pubmed-4905976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49059762016-07-04 A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression Nicolaou, Nicoletta Constandinou, Timothy G. Front Neuroinform Neuroscience Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C(NPMR), Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C(NPMR) on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C(NPMR) correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C(NPMR) is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications. Frontiers Media S.A. 2016-06-14 /pmc/articles/PMC4905976/ /pubmed/27378901 http://dx.doi.org/10.3389/fninf.2016.00019 Text en Copyright © 2016 Nicolaou and Constandinou. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Nicolaou, Nicoletta Constandinou, Timothy G. A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression |
title | A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression |
title_full | A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression |
title_fullStr | A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression |
title_full_unstemmed | A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression |
title_short | A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression |
title_sort | nonlinear causality estimator based on non-parametric multiplicative regression |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4905976/ https://www.ncbi.nlm.nih.gov/pubmed/27378901 http://dx.doi.org/10.3389/fninf.2016.00019 |
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