Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information
Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditio...
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Hindawi Publishing Corporation
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3363995/ |
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pubmed-33639952012-06-06 Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information Liu, Ying Aviyente, Selin Research Article Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between two time series. Compared to traditional causality detection methods based on linear models, directed information is a model-free measure and can detect both linear and nonlinear causality relationships. However, the effectiveness of using DI for capturing the causality in different models and neurophysiological data has not been thoroughly illustrated to date. In addition, the advantage of DI compared to model-based measures, especially those used to implement Granger causality, has not been fully investigated. In this paper, we address these issues by evaluating the performance of directed information on both simulated data sets and electroencephalogram (EEG) data to illustrate its effectiveness for quantifying the effective connectivity in the brain. Hindawi Publishing Corporation 2012 2012-05-16 /pmc/articles/PMC3363995/ /pubmed/22675401 http://dx.doi.org/10.1155/2012/635103 Text en Copyright © 2012 Y. Liu and S. Aviyente. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Liu, Ying Aviyente, Selin |
| spellingShingle |
Liu, Ying Aviyente, Selin Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information |
| author_facet |
Liu, Ying Aviyente, Selin |
| author_sort |
Liu, Ying |
| title |
Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information |
| title_short |
Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information |
| title_full |
Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information |
| title_fullStr |
Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information |
| title_full_unstemmed |
Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information |
| title_sort |
quantification of effective connectivity in the brain using a measure of directed information |
| description |
Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between two time series. Compared to traditional causality detection methods based on linear models, directed information is a model-free measure and can detect both linear and nonlinear causality relationships. However, the effectiveness of using DI for capturing the causality in different models and neurophysiological data has not been thoroughly illustrated to date. In addition, the advantage of DI compared to model-based measures, especially those used to implement Granger causality, has not been fully investigated. In this paper, we address these issues by evaluating the performance of directed information on both simulated data sets and electroencephalogram (EEG) data to illustrate its effectiveness for quantifying the effective connectivity in the brain. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3363995/ |
| _version_ |
1611533709623689216 |