Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI
Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information fr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Stramaglia, Sebastiano [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on biomedical engineering - New York, NY : IEEE, 1964, 63(2016), 12, Seite 2518-2524 |
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| Übergeordnetes Werk: |
volume:63 ; year:2016 ; number:12 ; pages:2518-2524 |
| Links: |
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| DOI / URN: |
10.1109/TBME.2016.2559578 |
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OLC1987454855 |
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| 245 | 1 | 0 | |a Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI |
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| 520 | |a Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. | ||
| 650 | 4 | |a functional magnetic resonance imaging (fMRI) | |
| 650 | 4 | |a Indexes | |
| 650 | 4 | |a Complex systems | |
| 650 | 4 | |a Redundancy | |
| 650 | 4 | |a Kernel | |
| 650 | 4 | |a Brain connectivity | |
| 650 | 4 | |a Complex networks | |
| 650 | 4 | |a Analytical models | |
| 650 | 4 | |a Time series analysis | |
| 650 | 4 | |a granger causality (GC) | |
| 650 | 4 | |a synergy | |
| 650 | 4 | |a Quantitative Biology | |
| 650 | 4 | |a Physics | |
| 650 | 4 | |a Neurons and Cognition | |
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Statistics and Probability | |
| 650 | 4 | |a Information Theory | |
| 650 | 4 | |a Data Analysis | |
| 700 | 1 | |a Angelini, Leonardo |4 oth | |
| 700 | 1 | |a Wu, Guorong |4 oth | |
| 700 | 1 | |a Cortes, Jesus M |4 oth | |
| 700 | 1 | |a Faes, Luca |4 oth | |
| 700 | 1 | |a Marinazzo, Daniele |4 oth | |
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10.1109/TBME.2016.2559578 doi PQ20170206 (DE-627)OLC1987454855 (DE-599)GBVOLC1987454855 (PRQ)a1625-6db2a0ed5172d843be861da224891bddbc010195e4bbfc839482539cb05b5a240 (KEY)0037705820160000063001202518synergeticandredundantinformationflowdetectedbyunn DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Stramaglia, Sebastiano verfasserin aut Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. functional magnetic resonance imaging (fMRI) Indexes Complex systems Redundancy Kernel Brain connectivity Complex networks Analytical models Time series analysis granger causality (GC) synergy Quantitative Biology Physics Neurons and Cognition Computer Science Statistics and Probability Information Theory Data Analysis Angelini, Leonardo oth Wu, Guorong oth Cortes, Jesus M oth Faes, Luca oth Marinazzo, Daniele oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 12, Seite 2518-2524 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:12 pages:2518-2524 http://dx.doi.org/10.1109/TBME.2016.2559578 Volltext http://ieeexplore.ieee.org/document/7462237 http://arxiv.org/abs/1504.03584 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 12 2518-2524 |
| spelling |
10.1109/TBME.2016.2559578 doi PQ20170206 (DE-627)OLC1987454855 (DE-599)GBVOLC1987454855 (PRQ)a1625-6db2a0ed5172d843be861da224891bddbc010195e4bbfc839482539cb05b5a240 (KEY)0037705820160000063001202518synergeticandredundantinformationflowdetectedbyunn DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Stramaglia, Sebastiano verfasserin aut Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. functional magnetic resonance imaging (fMRI) Indexes Complex systems Redundancy Kernel Brain connectivity Complex networks Analytical models Time series analysis granger causality (GC) synergy Quantitative Biology Physics Neurons and Cognition Computer Science Statistics and Probability Information Theory Data Analysis Angelini, Leonardo oth Wu, Guorong oth Cortes, Jesus M oth Faes, Luca oth Marinazzo, Daniele oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 12, Seite 2518-2524 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:12 pages:2518-2524 http://dx.doi.org/10.1109/TBME.2016.2559578 Volltext http://ieeexplore.ieee.org/document/7462237 http://arxiv.org/abs/1504.03584 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 12 2518-2524 |
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10.1109/TBME.2016.2559578 doi PQ20170206 (DE-627)OLC1987454855 (DE-599)GBVOLC1987454855 (PRQ)a1625-6db2a0ed5172d843be861da224891bddbc010195e4bbfc839482539cb05b5a240 (KEY)0037705820160000063001202518synergeticandredundantinformationflowdetectedbyunn DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Stramaglia, Sebastiano verfasserin aut Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. functional magnetic resonance imaging (fMRI) Indexes Complex systems Redundancy Kernel Brain connectivity Complex networks Analytical models Time series analysis granger causality (GC) synergy Quantitative Biology Physics Neurons and Cognition Computer Science Statistics and Probability Information Theory Data Analysis Angelini, Leonardo oth Wu, Guorong oth Cortes, Jesus M oth Faes, Luca oth Marinazzo, Daniele oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 12, Seite 2518-2524 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:12 pages:2518-2524 http://dx.doi.org/10.1109/TBME.2016.2559578 Volltext http://ieeexplore.ieee.org/document/7462237 http://arxiv.org/abs/1504.03584 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 12 2518-2524 |
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10.1109/TBME.2016.2559578 doi PQ20170206 (DE-627)OLC1987454855 (DE-599)GBVOLC1987454855 (PRQ)a1625-6db2a0ed5172d843be861da224891bddbc010195e4bbfc839482539cb05b5a240 (KEY)0037705820160000063001202518synergeticandredundantinformationflowdetectedbyunn DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Stramaglia, Sebastiano verfasserin aut Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. functional magnetic resonance imaging (fMRI) Indexes Complex systems Redundancy Kernel Brain connectivity Complex networks Analytical models Time series analysis granger causality (GC) synergy Quantitative Biology Physics Neurons and Cognition Computer Science Statistics and Probability Information Theory Data Analysis Angelini, Leonardo oth Wu, Guorong oth Cortes, Jesus M oth Faes, Luca oth Marinazzo, Daniele oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 12, Seite 2518-2524 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:12 pages:2518-2524 http://dx.doi.org/10.1109/TBME.2016.2559578 Volltext http://ieeexplore.ieee.org/document/7462237 http://arxiv.org/abs/1504.03584 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 12 2518-2524 |
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10.1109/TBME.2016.2559578 doi PQ20170206 (DE-627)OLC1987454855 (DE-599)GBVOLC1987454855 (PRQ)a1625-6db2a0ed5172d843be861da224891bddbc010195e4bbfc839482539cb05b5a240 (KEY)0037705820160000063001202518synergeticandredundantinformationflowdetectedbyunn DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Stramaglia, Sebastiano verfasserin aut Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. functional magnetic resonance imaging (fMRI) Indexes Complex systems Redundancy Kernel Brain connectivity Complex networks Analytical models Time series analysis granger causality (GC) synergy Quantitative Biology Physics Neurons and Cognition Computer Science Statistics and Probability Information Theory Data Analysis Angelini, Leonardo oth Wu, Guorong oth Cortes, Jesus M oth Faes, Luca oth Marinazzo, Daniele oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 12, Seite 2518-2524 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:12 pages:2518-2524 http://dx.doi.org/10.1109/TBME.2016.2559578 Volltext http://ieeexplore.ieee.org/document/7462237 http://arxiv.org/abs/1504.03584 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 12 2518-2524 |
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Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI |
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Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. |
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Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. |
| abstract_unstemmed |
Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits. |
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Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI |
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