IEICE TRANSACTIONS on Fundamentals
Functional Connectivity Estimation by Phase Synchronization and Information Flow Approaches in Coupled Chaotic Dynamical Systems
Summary :
Various types of indices for estimating functional connectivity have been developed over the years that have introduced effective approaches to discovering complex neural networks in the brain. Two significant examples are the phase lag index (PLI) and transfer entropy (TE). Both indices have specific benefits; PLI, defined using instantaneous phase dynamics, achieves high spatiotemporal resolution, whereas transfer entropy (TE), defined using information flow, reveals directed network characteristics. However, the relationship between these indices remains unclear. In this study, we hypothesize that there exists a complementary relationship between PLI and TE to discover new aspects of functional connectivity that cannot be detected using either PLI or TE. To validate this hypothesis, we evaluated the synchronization in a coupled Rössler model using PLI and TE. Consequently, we proved the existence of non-linear relationships between PLI and TE. Both indexes exhibit a specific trend that demonstrates a linear relationship in the region of small TE values. However, above a specific TE value, PLI converges to a constant irrespective of the TE value. In addition to this relational difference in synchronization, there is another characteristic difference between these indices. Moreover, by virtue of its finer temporal resolution, PLI can capture the temporal variability of the degree of synchronization, which is called dynamical functional connectivity. TE lacks this temporal characteristic because it requires a longer evaluation period in this estimation process. Therefore, combining the advantages of both indices might contribute to revealing complex spatiotemporal functional connectivity in brain activity.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.12 pp.1604-1611
- Publication Date
- 2022/12/01
- Publicized
- 2022/06/03
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021EAP1169
- Type of Manuscript
- PAPER
- Category
- Neural Networks and Bioengineering
Authors
Mayuna TOBE
Chiba Institute of Technology
Sou NOBUKAWA
Chiba Institute of Technology,National Institute of Mental Health, National Center of Neurology and Psychiatry
Keyword
Latest Issue
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Mayuna TOBE, Sou NOBUKAWA, "Functional Connectivity Estimation by Phase Synchronization and Information Flow Approaches in Coupled Chaotic Dynamical Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 12, pp. 1604-1611, December 2022, doi: 10.1587/transfun.2021EAP1169.
Abstract: Various types of indices for estimating functional connectivity have been developed over the years that have introduced effective approaches to discovering complex neural networks in the brain. Two significant examples are the phase lag index (PLI) and transfer entropy (TE). Both indices have specific benefits; PLI, defined using instantaneous phase dynamics, achieves high spatiotemporal resolution, whereas transfer entropy (TE), defined using information flow, reveals directed network characteristics. However, the relationship between these indices remains unclear. In this study, we hypothesize that there exists a complementary relationship between PLI and TE to discover new aspects of functional connectivity that cannot be detected using either PLI or TE. To validate this hypothesis, we evaluated the synchronization in a coupled Rössler model using PLI and TE. Consequently, we proved the existence of non-linear relationships between PLI and TE. Both indexes exhibit a specific trend that demonstrates a linear relationship in the region of small TE values. However, above a specific TE value, PLI converges to a constant irrespective of the TE value. In addition to this relational difference in synchronization, there is another characteristic difference between these indices. Moreover, by virtue of its finer temporal resolution, PLI can capture the temporal variability of the degree of synchronization, which is called dynamical functional connectivity. TE lacks this temporal characteristic because it requires a longer evaluation period in this estimation process. Therefore, combining the advantages of both indices might contribute to revealing complex spatiotemporal functional connectivity in brain activity.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1169/_p
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@ARTICLE{e105-a_12_1604,
author={Mayuna TOBE, Sou NOBUKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Functional Connectivity Estimation by Phase Synchronization and Information Flow Approaches in Coupled Chaotic Dynamical Systems},
year={2022},
volume={E105-A},
number={12},
pages={1604-1611},
abstract={Various types of indices for estimating functional connectivity have been developed over the years that have introduced effective approaches to discovering complex neural networks in the brain. Two significant examples are the phase lag index (PLI) and transfer entropy (TE). Both indices have specific benefits; PLI, defined using instantaneous phase dynamics, achieves high spatiotemporal resolution, whereas transfer entropy (TE), defined using information flow, reveals directed network characteristics. However, the relationship between these indices remains unclear. In this study, we hypothesize that there exists a complementary relationship between PLI and TE to discover new aspects of functional connectivity that cannot be detected using either PLI or TE. To validate this hypothesis, we evaluated the synchronization in a coupled Rössler model using PLI and TE. Consequently, we proved the existence of non-linear relationships between PLI and TE. Both indexes exhibit a specific trend that demonstrates a linear relationship in the region of small TE values. However, above a specific TE value, PLI converges to a constant irrespective of the TE value. In addition to this relational difference in synchronization, there is another characteristic difference between these indices. Moreover, by virtue of its finer temporal resolution, PLI can capture the temporal variability of the degree of synchronization, which is called dynamical functional connectivity. TE lacks this temporal characteristic because it requires a longer evaluation period in this estimation process. Therefore, combining the advantages of both indices might contribute to revealing complex spatiotemporal functional connectivity in brain activity.},
keywords={},
doi={10.1587/transfun.2021EAP1169},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - Functional Connectivity Estimation by Phase Synchronization and Information Flow Approaches in Coupled Chaotic Dynamical Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1604
EP - 1611
AU - Mayuna TOBE
AU - Sou NOBUKAWA
PY - 2022
DO - 10.1587/transfun.2021EAP1169
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2022
AB - Various types of indices for estimating functional connectivity have been developed over the years that have introduced effective approaches to discovering complex neural networks in the brain. Two significant examples are the phase lag index (PLI) and transfer entropy (TE). Both indices have specific benefits; PLI, defined using instantaneous phase dynamics, achieves high spatiotemporal resolution, whereas transfer entropy (TE), defined using information flow, reveals directed network characteristics. However, the relationship between these indices remains unclear. In this study, we hypothesize that there exists a complementary relationship between PLI and TE to discover new aspects of functional connectivity that cannot be detected using either PLI or TE. To validate this hypothesis, we evaluated the synchronization in a coupled Rössler model using PLI and TE. Consequently, we proved the existence of non-linear relationships between PLI and TE. Both indexes exhibit a specific trend that demonstrates a linear relationship in the region of small TE values. However, above a specific TE value, PLI converges to a constant irrespective of the TE value. In addition to this relational difference in synchronization, there is another characteristic difference between these indices. Moreover, by virtue of its finer temporal resolution, PLI can capture the temporal variability of the degree of synchronization, which is called dynamical functional connectivity. TE lacks this temporal characteristic because it requires a longer evaluation period in this estimation process. Therefore, combining the advantages of both indices might contribute to revealing complex spatiotemporal functional connectivity in brain activity.
ER -
English
