Multistability in Large Scale Models of Brain Activity

PLoS Comput Biol. 2015 Dec 28;11(12):e1004644. doi: 10.1371/journal.pcbi.1004644. eCollection 2015 Dec.


Noise driven exploration of a brain network's dynamic repertoire has been hypothesized to be causally involved in cognitive function, aging and neurodegeneration. The dynamic repertoire crucially depends on the network's capacity to store patterns, as well as their stability. Here we systematically explore the capacity of networks derived from human connectomes to store attractor states, as well as various network mechanisms to control the brain's dynamic repertoire. Using a deterministic graded response Hopfield model with connectome-based interactions, we reconstruct the system's attractor space through a uniform sampling of the initial conditions. Large fixed-point attractor sets are obtained in the low temperature condition, with a bigger number of attractors than ever reported so far. Different variants of the initial model, including (i) a uniform activation threshold or (ii) a global negative feedback, produce a similarly robust multistability in a limited parameter range. A numerical analysis of the distribution of the attractors identifies spatially-segregated components, with a centro-medial core and several well-delineated regional patches. Those different modes share similarity with the fMRI independent components observed in the "resting state" condition. We demonstrate non-stationary behavior in noise-driven generalizations of the models, with different meta-stable attractors visited along the same time course. Only the model with a global dynamic density control is found to display robust and long-lasting non-stationarity with no tendency toward either overactivity or extinction. The best fit with empirical signals is observed at the edge of multistability, a parameter region that also corresponds to the highest entropy of the attractors.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology
  • Animals
  • Biological Clocks / physiology*
  • Brain / physiology*
  • Computer Simulation
  • Connectome / methods*
  • Humans
  • Models, Neurological*
  • Models, Statistical*
  • Nerve Net / physiology*

Grants and funding

The research reported here was supported by Brain Network Recovery Group funded by the James S McDonnel Foundation; Award Number: 220020255 to VJ; A*Midex grant Coord-Age, to VJ; and European Union Seventh Framework Programme (Human Brain Project); Award Number: 60402 to VJ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.