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Global segregation of cortical activity and metastable dynamics.

Stratton P, Wiles J - Front Syst Neurosci (2015)

Bottom Line: The model also explains how mutually-exclusive activity could arise across large portions of the cortex, such as between the default-mode and task-positive networks.It is robust to noise but does not require noise to autonomously generate metastability.We conclude that the long range segregation observed in brain activity and required for global metastable dynamics could be provided by the thalamocortical matrix, and is strongly modulated by brainstem input to the thalamus.

View Article: PubMed Central - PubMed

Affiliation: Queensland Brain Institute, The University of Queensland Brisbane, QLD, Australia ; Centre for Clinical Research, The University of Queensland Brisbane, QLD, Australia.

ABSTRACT
Cortical activity exhibits persistent metastable dynamics. Assemblies of neurons transiently couple (integrate) and decouple (segregate) at multiple spatiotemporal scales; both integration and segregation are required to support metastability. Integration of distant brain regions can be achieved through long range excitatory projections, but the mechanism supporting long range segregation is not clear. We argue that the thalamocortical matrix connections, which project diffusely from the thalamus to the cortex and have long been thought to support cortical gain control, play an equally-important role in cortical segregation. We present a computational model of the diffuse thalamocortical loop, called the competitive cross-coupling (CXC) spiking network. Simulations of the model show how different levels of tonic input from the brainstem to the thalamus could control dynamical complexity in the cortex, directing transitions between sleep, wakefulness and high attention or vigilance. The model also explains how mutually-exclusive activity could arise across large portions of the cortex, such as between the default-mode and task-positive networks. It is robust to noise but does not require noise to autonomously generate metastability. We conclude that the long range segregation observed in brain activity and required for global metastable dynamics could be provided by the thalamocortical matrix, and is strongly modulated by brainstem input to the thalamus.

No MeSH data available.


Changing input level from the AAS dramatically affected network dynamics. Left panels—AAS input levels from 0 to 10. Right panels—close-up on AAS input from 0 to 1. Cases a–c from Figure 3 are marked on the graph (top). Very low input levels below 0.6 usually resulted in no network activity, with sporadic instances of complex dynamics occurring for AAS input levels between 0.35 and 0.6 (see close-up panels on right). At input levels between 0.6 and 0.8, global entrainment at high firing rates but low oscillation frequencies emerged abruptly, again with some sporadic interspersed instances of complex dynamics. The sporadic large changes in firing rate and trapping time for low AAS input levels (between 0.35 and 0.8) are characteristic of network dynamics being bistable, with the random initial conditions for each network instance controlling which of the stable states the network settled into in each case. A small increment in AAS input could therefore result in a large change in dynamics, as can be seen in the close-up panels on the right. At AAS levels beyond 0.8, a sustained switch to complex dynamics occurred (i.e., the bistability vanished). At high levels of AAS input, firing rate and trapping time increased. Adding noise to the neural membranes removed the bistability and caused complex dynamics to be sustained for all levels of AAS input down to zero (dashed line, top right).
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Figure 4: Changing input level from the AAS dramatically affected network dynamics. Left panels—AAS input levels from 0 to 10. Right panels—close-up on AAS input from 0 to 1. Cases a–c from Figure 3 are marked on the graph (top). Very low input levels below 0.6 usually resulted in no network activity, with sporadic instances of complex dynamics occurring for AAS input levels between 0.35 and 0.6 (see close-up panels on right). At input levels between 0.6 and 0.8, global entrainment at high firing rates but low oscillation frequencies emerged abruptly, again with some sporadic interspersed instances of complex dynamics. The sporadic large changes in firing rate and trapping time for low AAS input levels (between 0.35 and 0.8) are characteristic of network dynamics being bistable, with the random initial conditions for each network instance controlling which of the stable states the network settled into in each case. A small increment in AAS input could therefore result in a large change in dynamics, as can be seen in the close-up panels on the right. At AAS levels beyond 0.8, a sustained switch to complex dynamics occurred (i.e., the bistability vanished). At high levels of AAS input, firing rate and trapping time increased. Adding noise to the neural membranes removed the bistability and caused complex dynamics to be sustained for all levels of AAS input down to zero (dashed line, top right).

Mentions: Mean firing rate increased linearly as AAS input was increased (Figure 4, top), except for low input values where firing rate was typically either zero or extremely high. The regions of zero network firing were generally for very low AAS input values near zero, while regions of very high firing rate occurred for slightly higher input levels between 0.6 and 0.8. The transformation between low and high firing rates was sudden, with no intermediate states. The dynamics in the high firing rate regions were as shown in Figure 3C—low complexity, global entrainment, and slow oscillations around 5 Hz. In these high firing rate regions the trapping time was high (Figure 4, bottom), signifying that the network state was not changing (marked as point c in Figure 4).


Global segregation of cortical activity and metastable dynamics.

Stratton P, Wiles J - Front Syst Neurosci (2015)

Changing input level from the AAS dramatically affected network dynamics. Left panels—AAS input levels from 0 to 10. Right panels—close-up on AAS input from 0 to 1. Cases a–c from Figure 3 are marked on the graph (top). Very low input levels below 0.6 usually resulted in no network activity, with sporadic instances of complex dynamics occurring for AAS input levels between 0.35 and 0.6 (see close-up panels on right). At input levels between 0.6 and 0.8, global entrainment at high firing rates but low oscillation frequencies emerged abruptly, again with some sporadic interspersed instances of complex dynamics. The sporadic large changes in firing rate and trapping time for low AAS input levels (between 0.35 and 0.8) are characteristic of network dynamics being bistable, with the random initial conditions for each network instance controlling which of the stable states the network settled into in each case. A small increment in AAS input could therefore result in a large change in dynamics, as can be seen in the close-up panels on the right. At AAS levels beyond 0.8, a sustained switch to complex dynamics occurred (i.e., the bistability vanished). At high levels of AAS input, firing rate and trapping time increased. Adding noise to the neural membranes removed the bistability and caused complex dynamics to be sustained for all levels of AAS input down to zero (dashed line, top right).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4548222&req=5

Figure 4: Changing input level from the AAS dramatically affected network dynamics. Left panels—AAS input levels from 0 to 10. Right panels—close-up on AAS input from 0 to 1. Cases a–c from Figure 3 are marked on the graph (top). Very low input levels below 0.6 usually resulted in no network activity, with sporadic instances of complex dynamics occurring for AAS input levels between 0.35 and 0.6 (see close-up panels on right). At input levels between 0.6 and 0.8, global entrainment at high firing rates but low oscillation frequencies emerged abruptly, again with some sporadic interspersed instances of complex dynamics. The sporadic large changes in firing rate and trapping time for low AAS input levels (between 0.35 and 0.8) are characteristic of network dynamics being bistable, with the random initial conditions for each network instance controlling which of the stable states the network settled into in each case. A small increment in AAS input could therefore result in a large change in dynamics, as can be seen in the close-up panels on the right. At AAS levels beyond 0.8, a sustained switch to complex dynamics occurred (i.e., the bistability vanished). At high levels of AAS input, firing rate and trapping time increased. Adding noise to the neural membranes removed the bistability and caused complex dynamics to be sustained for all levels of AAS input down to zero (dashed line, top right).
Mentions: Mean firing rate increased linearly as AAS input was increased (Figure 4, top), except for low input values where firing rate was typically either zero or extremely high. The regions of zero network firing were generally for very low AAS input values near zero, while regions of very high firing rate occurred for slightly higher input levels between 0.6 and 0.8. The transformation between low and high firing rates was sudden, with no intermediate states. The dynamics in the high firing rate regions were as shown in Figure 3C—low complexity, global entrainment, and slow oscillations around 5 Hz. In these high firing rate regions the trapping time was high (Figure 4, bottom), signifying that the network state was not changing (marked as point c in Figure 4).

Bottom Line: The model also explains how mutually-exclusive activity could arise across large portions of the cortex, such as between the default-mode and task-positive networks.It is robust to noise but does not require noise to autonomously generate metastability.We conclude that the long range segregation observed in brain activity and required for global metastable dynamics could be provided by the thalamocortical matrix, and is strongly modulated by brainstem input to the thalamus.

View Article: PubMed Central - PubMed

Affiliation: Queensland Brain Institute, The University of Queensland Brisbane, QLD, Australia ; Centre for Clinical Research, The University of Queensland Brisbane, QLD, Australia.

ABSTRACT
Cortical activity exhibits persistent metastable dynamics. Assemblies of neurons transiently couple (integrate) and decouple (segregate) at multiple spatiotemporal scales; both integration and segregation are required to support metastability. Integration of distant brain regions can be achieved through long range excitatory projections, but the mechanism supporting long range segregation is not clear. We argue that the thalamocortical matrix connections, which project diffusely from the thalamus to the cortex and have long been thought to support cortical gain control, play an equally-important role in cortical segregation. We present a computational model of the diffuse thalamocortical loop, called the competitive cross-coupling (CXC) spiking network. Simulations of the model show how different levels of tonic input from the brainstem to the thalamus could control dynamical complexity in the cortex, directing transitions between sleep, wakefulness and high attention or vigilance. The model also explains how mutually-exclusive activity could arise across large portions of the cortex, such as between the default-mode and task-positive networks. It is robust to noise but does not require noise to autonomously generate metastability. We conclude that the long range segregation observed in brain activity and required for global metastable dynamics could be provided by the thalamocortical matrix, and is strongly modulated by brainstem input to the thalamus.

No MeSH data available.