Criticality in RSNs

R. Ton, G. Deco, A.D. Daffertshofer

    Research output: Contribution to ConferencePosterOther research output


    On the multiple aspects of criticality

    Ton, Robert; Deco, Gustavo; Daffertshofer, Andreas

    Brain activity at rest displays well-structured spatio-temporal patterns. These patterns have been shown to have clinical and functional relevance[1] and are believed to provide insight into the general architecture of the human brain.

    Several studies using M/EEG suggest that the brain operates at a critical state. Hallmarks of critical systems are scale-free (spectral) distribution functions that typically obey power laws [2] and may stem from multi-scale entropies [3]. In simulations studies, criticality is also an important notion. There that notion refers to a critical value of the control parameter leading to isolated bifurcations in model behavior. A very consistent finding is that correlations between model and data are maximal at the cusp of such bifurcations [4,5]. In fact, that led to the idea that the brain operates close to critical states. Network dynamics close to such a parameter regime do show critical dynamics [6] and attractor multi-stability [7] – but is criticality therefore mandatory for the brain to function optimally? Being so close to the critical parameter might come with high risk for catastrophic events. Does this put this ‘optimality’ at stake?.

    Using a phase reduction approach to a network of oscillating neural masses we observed a (time-delayed) balance between attracting and repulsive force. We argue that this balance causes network behavior to be insensitive to coupling strength and only moderately (and continuously) dependent on delay. The network dynamics suggests that our model is in a permanently critical state, as evidenced by a broad – or even flat – potential of the drift coefficient in the corresponding Fokker-Planck equation describing the evolution of the probability density of the degree of synchrony across the network.

    We conclude that critical behavior of brain activity does not necessarily imply that the brain operates at the cusp of isolated bifurcations. Our model suggests that the experimentally observed criticality is the result of a self-organization rendering parameter specifics less crucial for the brain’s macroscopic behavior.

    1. Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, Barnes GR, Owen JP, Morris PG, Nagarajan SS: Measuring functional connectivity using MEG: Methodology and comparison with fcMRI. Neuroimage 2000, 56: 1082-1104
    2. ¬Linkenkaer-Hansen K, Nikouline VV, Palva JM, Ilmoniemi RJ: Longe-range temporal correlations and scaling behavior in human brain oscillations. The Journal of Neuroscience 2001, 21(4):1370-1377.
    3. McIntosh AR, Kovacevic N, Itier RJ: Increased brain signal variability accompanies lower behavorial variability in development. PLOS Computational Biology 2008, 4(7): e1000106.
    4. Ghosh A, Rho Y, Mcintosh AR, Kötter R, Jirsa VK:Noise during rest enables the exploration of the brain's dynamic repertoire. PLOS Computational Biology 2008, 4(10): e1000196.
    5. Deco G Ponce-Alvarez A, Mantini D, Romani GL, Hagman P, Corbetta M: Resting-state functional connectivity emerges from structurally and dynamicall shaped slow linear fluctuations. The Journal of Neuroscience 2013, 33(27): 11239-11252.
    6. Botcharova M, Farmer SF, Berthouze L: Power-law distribution of phase-locking intervals does not imply critical interaction. Physical Review E 2012, 86: 051920.
    7. Deco G, Jirsa VK: Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors. The Journal of Neuroscience 2012, 32: 3366-3375.
    Original languageEnglish
    Publication statusPublished - 2014
    EventMove Research Meeting 2014 -
    Duration: 30 Jan 201430 Jan 2014


    ConferenceMove Research Meeting 2014


    Dive into the research topics of 'Criticality in RSNs'. Together they form a unique fingerprint.

    Cite this