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Biological Cybernetics

, Volume 112, Issue 1–2, pp 153–161 | Cite as

Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective

  • Matthieu Gilson
Prospects

Abstract

Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input–output mapping—determined by EC—for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data—movie viewing versus resting state—illustrates that changes in local variability and changes in brain coordination go hand in hand.

Keywords

Whole-brain dynamic model FMRI data Covariance coding 

Notes

Acknowledgements

The author thanks Moritz Deger and Martin Nawrot for organizing the 12th International Neural Coding Workshop, NC2016. The author is also grateful to Pierre Yger, Ruben Moreno-Bote, Vicente Pallarez, Andrea Insabato, Gustavo Deco and Morten Kringelbach for constructive discussions.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Universitat Pompeu FabraBarcelonaSpain

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