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Rethinking segregation and integration: contributions of whole-brain modelling

Nature Reviews Neuroscience volume 16, pages 430–439 (2015)Cite this article

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Abstract

The brain regulates information flow by balancing the segregation and integration of incoming stimuli to facilitate flexible cognition and behaviour. The topological features of brain networks — in particular, network communities and hubs — support this segregation and integration but do not provide information about how external inputs are processed dynamically (that is, over time). Experiments in which the consequences of selective inputs on brain activity are controlled and traced with great precision could provide such information. However, such strategies have thus far had limited success. By contrast, recent whole-brain computational modelling approaches have enabled us to start assessing the effect of input perturbations on brain dynamics in silico.

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Figure 1: Segregation and integration measures can be improved using whole-brain modelling.
Figure 2: Using whole-brain computational modelling.
Figure 3: Using the binding to extend our understanding of integration in the human brain.
Figure 4: Using perturbational segregation and integration measures to characterize health and disease.

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Acknowledgements

G.D. is supported by the European Research Council (ERC) Advanced grant: DYSTRUCTURE (no. 295129), by the Spanish Research Project SAF2010-16085, by the FP7-ICT BrainScales and by the Brain Network Recovery Group through the James S. McDonnell Foundation. G.T. is supported by the Paul Allen Family Foundation and by the James S. McDonnell Foundation. M.B. is supported by the Mind Science Foundation. M.L.K. is supported by the ERC Consolidator grant: CAREGIVING (no. 615539) and by the TrygFonden Charitable Foundation. The authors thank P. Maquet for agreeing to share the previously published sleep and wakefulness functional MRI data for the purposes of this article.

Author information

Authors and Affiliations

  1. Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain,

    Gustavo Deco

  2. and the Instituci Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Passeig Llus Companys 23, Barcelona 08010, Spain.,

    Gustavo Deco

  3. Department of Psychiatry, University of Wisconsin-Madison, Wisconsin, 53719, USA

    Giulio Tononi & Melanie Boly

  4. Department of Neurology, University of Wisconsin-Madison, Wisconsin, 53702, USA

    Melanie Boly

  5. Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK,

    Morten L. Kringelbach

  6. the Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus C, 8000, Denmark

    Morten L. Kringelbach

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  1. Gustavo Deco
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  2. Giulio Tononi
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Corresponding author

Correspondence to Gustavo Deco.

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FURTHER INFORMATION

The Virtual Brain

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Glossary

Bifurcation

One of the basic tools to analyse dynamic systems. It is defined by qualitative changes in the asymptotic behaviour of the system ('attractors') under parameter variation.

Diffusion tensor imaging

(DTI). An MRI technique that takes advantage of the restricted diffusion of water through myelinated nerve fibres in the brain to enable inference of the anatomical connectivity between regions of the brain.

Edges

In a brain graph, edges denote anatomical or functional connections between nodes, which may indicate brain regions or neurons.

Graph theory

A branch of mathematics that deals with the formal description and analysis of graphs. A graph is simply defined as a set of nodes (vertices) that are linked by connections (edges) and can be directed or undirected.

Magnetoencephalography

(MEG). A method of measuring brain activity that involves the detection of minute perturbations in the extracranial magnetic field that are generated by the electrical activity of neuronal populations.

Mean-field models

Mean-field approximations consist of replacing the temporally averaged discharge rate of a cell with an equivalent momentary activity of a neural population (the ensemble average) that corresponds to the assumption of ergodicity. According to these approximations, each cell assembly is characterized by its activity population rate.

Metastability

In dynamic systems, metastability refers to a state that falls outside the natural equilibrium state of the system but persists for an extended period of time.

Small-world architecture

This term is used to describe complex networks that have a combination of random and regular topological properties.

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Deco, G., Tononi, G., Boly, M. et al. Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci 16, 430–439 (2015). https://doi.org/10.1038/nrn3963

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