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  Estimation of directed effective connectivity from fMRI functional connectivity hints at asymmetries of cortical connectome

Gilson, M., Moreno-Bote, R., Ponce-Alvarez, A., Ritter, P., & Deco, G. (2016). Estimation of directed effective connectivity from fMRI functional connectivity hints at asymmetries of cortical connectome. PLoS Computational Biology, 12(3): e1004762. doi:10.1371/journal.pcbi.1004762.

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Gilson, Matthieu1, Author
Moreno-Bote, Ruben2, 3, 4, 5, Author
Ponce-Alvarez, Adrián1, Author
Ritter, Petra6, 7, 8, 9, Author              
Deco, Gustavo1, 10, Author
1Department of Technology and Information, Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain, ou_persistent22              
2University of Barcelona, Spain, ou_persistent22              
3Centro de Investigación en Red de Salud Mental (CIBERSAM), Sant Boi de Llobregat, Spain, ou_persistent22              
4Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain, ou_persistent22              
5Serra Húnter Fellow Programme, University Pompeu Fabra, Barcelona, Spain, ou_persistent22              
6Minerva Research Group Brain Modes, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_751546              
7Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
8State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
9Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
10Catalan Institution for Research and Advanced Studies (ICREA), University of Barcelona, Spain, ou_persistent22              


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 Abstract: The brain exhibits complex spatio-temporal patterns of activity. This phenomenon is governed by an interplay between the internal neural dynamics of cortical areas and their connectivity. Uncovering this complex relationship has raised much interest, both for theory and the interpretation of experimental data (e.g., fMRI recordings) using dynamical models. Here we focus on the so-called inverse problem: the inference of network parameters in a cortical model to reproduce empirically observed activity. Although it has received a lot of interest, recovering directed connectivity for large networks has been rather unsuccessful so far. The present study specifically addresses this point for a noise-diffusion network model. We develop a Lyapunov optimization that iteratively tunes the network connectivity in order to reproduce second-order moments of the node activity, or functional connectivity. We show theoretically and numerically that the use of covariances with both zero and non-zero time shifts is the key to infer directed connectivity. The first main theoretical finding is that an accurate estimation of the underlying network connectivity requires that the time shift for covariances is matched with the time constant of the dynamical system. In addition to the network connectivity, we also adjust the intrinsic noise received by each network node. The framework is applied to experimental fMRI data recorded for subjects at rest. Diffusion-weighted MRI data provide an estimate of anatomical connections, which is incorporated to constrain the cortical model. The empirical covariance structure is reproduced faithfully, especially its temporal component (i.e., time-shifted covariances) in addition to the spatial component that is usually the focus of studies. We find that the cortical interactions, referred to as effective connectivity, in the tuned model are not reciprocal. In particular, hubs are either receptors or feeders: they do not exhibit both strong incoming and outgoing connections. Our results sets a quantitative ground to explore the propagation of activity in the cortex.


Language(s): eng - English
 Dates: 2015-05-182016-01-202016-03-16
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1004762
PMID: 26982185
PMC: PMC4794215
Other: eCollection 2016
 Degree: -



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Title: PLoS Computational Biology
Source Genre: Journal
Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 12 (3) Sequence Number: e1004762 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1