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Related Experiment Video

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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Multistability in Large Scale Models of Brain Activity.

Mathieu Golos1, Viktor Jirsa1, Emmanuel Daucé1,2

  • 1Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France.

Plos Computational Biology
|December 29, 2015
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Summary
This summary is machine-generated.

Brain network dynamics, crucial for cognition, were explored using a Hopfield model. Findings reveal extensive attractor states and a novel control mechanism for stable, non-stationary brain activity.

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Area of Science:

  • Computational Neuroscience
  • Network Science
  • Cognitive Neuroscience

Background:

  • Brain network dynamics are hypothesized to underlie cognitive functions, aging, and neurodegeneration.
  • The capacity to store and stabilize patterns (attractor states) is critical for these dynamics.

Purpose of the Study:

  • To systematically explore human connectome-derived networks' capacity for storing attractor states.
  • To investigate network mechanisms controlling the brain's dynamic repertoire.
  • To model brain activity patterns and their stability.

Main Methods:

  • Utilized a deterministic graded response Hopfield model with connectome-based interactions.
  • Reconstructed the attractor space by uniformly sampling initial conditions.
  • Analyzed network variants with different activation thresholds and feedback mechanisms.
  • Investigated noise-driven generalizations and dynamic density control.

Main Results:

  • Discovered large sets of fixed-point attractors, exceeding previously reported numbers, especially at low temperatures.
  • Identified spatially segregated attractor components resembling fMRI resting-state components.
  • Demonstrated non-stationary behavior in noise-driven models, with a global dynamic density control model showing robust, long-lasting non-stationarity.
  • Found optimal fit with empirical signals at the edge of multistability, correlating with highest attractor entropy.

Conclusions:

  • Human connectome networks possess a vast capacity for storing attractor states.
  • Network mechanisms, particularly global dynamic density control, can regulate brain dynamics robustly.
  • The edge of multistability represents a critical parameter region for healthy brain function, balancing stability and complexity.