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On nodes and modes in resting state fMRI.

Karl J Friston1, Joshua Kahan2, Adeel Razi3

  • 1The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.

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Summary

This study reveals that intrinsic brain networks arise from dynamically unstable modes in brain connectivity. These modes, when combined, generate scale-free brain activity patterns observed in resting-state functional magnetic resonance imaging (fMRI).

Keywords:
BayesianCriticalityDynamic causal modellingEffective connectivityFree energyFunctional connectivityLyapunov exponentsProximity graphResting stateSelf-organisationfMRI

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Area of Science:

  • Neuroscience
  • Complex Systems
  • Computational Biology

Background:

  • Resting-state functional magnetic resonance imaging (fMRI) reveals intrinsic brain networks.
  • Neuronal fluctuations can be simulated using models based on the brain's connectome.
  • Understanding the modes of distributed activity underlying functional connectivity is crucial.

Purpose of the Study:

  • To investigate the relationship between functional and effective connectivity in intrinsic brain networks.
  • To demonstrate that eigenmodes of functional connectivity correspond to dynamically unstable modes of effective connectivity.
  • To explore the emergence of scale-free brain activity spectra from these modes.

Main Methods:

  • Analysis of eigenmodes of functional and effective connectivity using simulated and empirical fMRI data.
  • Application of dynamic causal modeling (DCM) to resting-state fluctuations.
  • Parameterization of effective connectivity using eigenmodes and Lyapunov exponents.

Main Results:

  • Eigenmodes of functional connectivity match eigenmodes of effective connectivity under symmetry constraints.
  • Principal modes of functional connectivity are dynamically unstable, exhibiting slow decay and long-term memory (small negative Lyapunov exponents).
  • Superposition of modes with exponents from a power-law distribution generates 1/f (scale-free) spectra.

Conclusions:

  • Dynamical instability is a key feature of intrinsic brain networks, potentially arising inevitably from systems separated by a Markov blanket.
  • Endogenous brain fluctuations are dominated by a small number of dynamically unstable modes.
  • Dynamic causal modeling using eigenmodes and Lyapunov exponents can estimate connectivity, topography, and dimensionality of the brain's scaling space.