TY - JOUR
T1 - Network analysis of whole-brain fMRI dynamics
T2 - A new framework based on dynamic communicability
AU - Gilson, Matthieu
AU - Kouvaris, Nikos E.
AU - Deco, Gustavo
AU - Mangin, Jean François
AU - Poupon, C.
AU - Lefranc, Sandrine
AU - Rivière, D.
AU - Zamora-López, G.
N1 - Funding Information:
This work has been supported by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 720270 (Human Brain Project SGA1) and No. 785907 (Human Brain Project SGA2), concerning MG, GZL, NEK and GD. MG also acknowledges funding from the Marie Skłodowska-Curie Action (Grant H2020-MSCA-656547 ) of the European Commission. GD also acknowledges funding from the Spanish Ministry Research Project PSI2016-75688-P (AEI/FEDER) and by the and by the Catalan Research Group Support 2017 SGR 1545. NEK acknowledges support from the “ MOVE-IN Louvain ” fellowship co-funded by the Marie Skłodowska-Curie Action of the European Commission.
Funding Information:
The “CONNECT/Archi Database” is the property of the CEA I2BM NeuroSpin centre and was designed under the supervision of Dr Cyril Poupon and Dr Jean-François Mangin. It was funded by the Federative Research Institute 49 , by the HIPPIP European grant, and the European CONNECT project ( http://www.brain-connect.eu ). Acquisitions were performed by the scientists involved in the Multi-scale Brain Architecture research program of NeuroSpin and by the staff of the UNIACT Laboratory of NeuroSpin (headed by Dr Lucie Hertz-Pannier), under the ethical approval CPP100002/CPP100022 (principal investigator Dr Denis Le Bihan). Access to the database can be requested from cyril.poupon@cea.fr .
Funding Information:
This work has been supported by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 720270 (Human Brain Project SGA1) and No. 785907 (Human Brain Project SGA2), concerning MG, GZL, NEK and GD. MG also acknowledges funding from the Marie Sk?odowska-Curie Action (Grant H2020-MSCA-656547) of the European Commission. GD also acknowledges funding from the Spanish Ministry Research Project PSI2016-75688-P (AEI/FEDER) and by the and by the Catalan Research Group Support 2017 SGR 1545. NEK acknowledges support from the ?MOVE-IN Louvain? fellowship co-funded by the Marie Sk?odowska-Curie Action of the European Commission. The ?CONNECT/Archi Database? is the property of the CEA I2BM NeuroSpin centre and was designed under the supervision of Dr Cyril Poupon and Dr Jean-Fran?ois Mangin. It was funded by the Federative Research Institute 49, by the HIPPIP European grant, and the European CONNECT project (http://www.brain-connect.eu). Acquisitions were performed by the scientists involved in the Multi-scale Brain Architecture research program of NeuroSpin and by the staff of the UNIACT Laboratory of NeuroSpin (headed by Dr Lucie Hertz-Pannier), under the ethical approval CPP100002/CPP100022 (principal investigator Dr Denis Le Bihan). Access to the database can be requested from cyril.poupon@cea.fr.
Publisher Copyright:
© 2019 Elsevier Inc.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time, moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.
AB - Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time, moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.
UR - http://www.scopus.com/inward/record.url?scp=85069843914&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2019.116007
DO - 10.1016/j.neuroimage.2019.116007
M3 - Article
C2 - 31306771
AN - SCOPUS:85069843914
SN - 1053-8119
VL - 201
SP - 116007
JO - NeuroImage
JF - NeuroImage
M1 - 116007
ER -