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• W2890388927 startingPage "421883" @default.
• W2890388927 abstract "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 process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for EC as well as input properties (similar to local excitabilities). The network analysis is then based on the network response (or Green function) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptors -communicability and flow- 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 (in which input properties play a role), 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." @default.
• W2890388927 created "2018-09-27" @default.
• W2890388927 creator A5009816991 @default.
• W2890388927 creator A5035699131 @default.
• W2890388927 creator A5041371472 @default.
• W2890388927 creator A5047963275 @default.
• W2890388927 creator A5049489554 @default.
• W2890388927 creator A5072376874 @default.
• W2890388927 creator A5090181826 @default.
• W2890388927 creator A5091205167 @default.
• W2890388927 date "2018-09-19" @default.
• W2890388927 modified "2023-09-27" @default.
• W2890388927 title "Analysis of brain network dynamics estimated from fMRI data: A new framework based on communicability and flow" @default.
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