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Ongoing cortical activity at rest: criticality, multistability, and ghost attractors.

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Brain resting state networks emerge from structured noise fluctuations in a spiking neural network model. This model, incorporating neuroanatomy, reveals a dynamic repertoire inherent in brain connectivity.

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Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Neuroimaging

Background:

  • The brain exhibits structured spatiotemporal patterns at rest, known as resting state networks (RSNs).
  • These RSNs show low-frequency (<0.1 Hz) fluctuations in blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals.
  • Understanding the mechanistic origins of these resting-state dynamics is crucial.

Purpose of the Study:

  • To investigate the origins of resting state activity using a global spiking attractor network model.
  • To explore how neuroanatomical connectivity shapes emergent functional connectivity.
  • To identify the conditions under which RSNs arise from network dynamics.

Main Methods:

  • Developed a mechanistic model of the brain using spiking neurons and realistic synaptic dynamics (AMPA, NMDA, GABA).
  • Integrated biologically realistic diffusion tensor imaging/diffusion spectrum imaging-derived neuroanatomical connectivity into the network model.
  • Simulated network activity to assess emergent functional connectivity and compare it with experimental BOLD-fMRI data.

Main Results:

  • The model accurately reproduced experimentally observed resting state functional connectivity in humans.
  • Optimal quantitative fit was achieved when the network operated at the edge of instability.
  • RSNs emerged as structured, low-frequency (<0.1 Hz) noise fluctuations around a stable equilibrium state.

Conclusions:

  • Resting state networks can emerge from the intrinsic dynamics of a spiking neural network coupled with realistic neuroanatomy.
  • The brain's functional repertoire is inherently present within its structural connectivity, manifested as latent multistable attractors.
  • Network operation near the edge of instability is key for generating structured resting-state dynamics.