The modulation of neural gain facilitates a transition between functional segregation and integration in the brain
- Stanford University, United States
- The University of Sydney, Australia
- QIMR Berghofer Medical Research Institute, Australia
- Metro North Mental Health Service, Australia
Figures
Figure 1
Manipulating neural gain.
(a) the Yerkes-Dodson relationship linking activity in the locus coeruleus nucleus to cognitive performance; (b) neural gain is modeled by a parameter (σ) that increases the maximum slope of the transfer function between incoming and outgoing activity within a brain region; (c) excitability is modeled by a parameter (γ) that amplifies the level of output; (d) the approach presently used to estimate network topology from the biophysical model.
Figure 2 with 4 supplements
Network Integration and Phase Synchrony.
(a) mean participation as a function of σ and γ; (b) phase synchrony (ρ) as a function of σ and γ; (c) mean participation (BA) aligned to the critical point (represented here as a dotted line) as a function of increasing σ; (d) BA aligned to the critical point as a function of increasing γ – the left and right dotted lines depicts the synchrony change at low and high γ, respectively. The y-axis in (c) and (d) represents the distance in parameter space aligned to the critical point/bifurcation for either σ (ΔσCB; mean across 0.2 ≤ γ ≤0.6) or γ (ΔγCB; mean across 0.3 ≤ σ ≤1.0). Lines are colored according to the state of phase synchrony on either side of the bifurcation (blue: low synchrony; yellow: high synchrony).
Figure 2—figure supplement 1
Relationship between phase regimen boundary and alternative measures of network integration.
(a-c) the inverse modularity (Q−1) was maximal following the σ boundary (ΔσCP; mean across 0.2 ≤ γ ≤0.6) and the immediately prior to the abrupt phase transition at high γ (ΔγCP; mean across 0.3 ≤ σ ≤1.0); (d-f) global efficiency (G.E.) was maximally variable with increasing σ and across the critical boundary at high γ.
Figure 2—figure supplement 2
Standard deviation of the order parameter across the parameter space.
(a) standard deviation of order parameter across the parameter space; (b) fluctuation scaling pre-boundary (σ = 0.375 and γ = 0.50); and (c) post-boundary (σ = 0.50 and γ = 0.575) – the thin blue line denotes a Pareto (i.e., power law) scaling effect.
Figure 2—figure supplement 3
Transition to self-sustained oscillations in a single brain region.
For the generic 2D oscillator model this shows the real parts of eigenvalues at equilibrium as the level of input (Iapp) to a region is increased. A transition to self-sustained oscillations in a local region occurs where this curve crosses zero. That regime is bounded by supercritical Hopf bifurcations at Iapp = 2.0 and Iapp = 14.
Figure 2—figure supplement 4
Average time-averaged connectivity matrix in regions of the parameter space associated with high (yellow) or low (blue) ordered phase synchrony.
https://doi.org/10.7554/eLife.31130.007
Figure 3
Topological and temporal relationships with phase regimen boundary.
(a-c) network communicability was maximal following the σ boundary (ΔσCP; mean across 0.2 ≤ γ ≤0.6) and the immediately prior to the abrupt phase transition at high γ (ΔγCP; mean across 0.3 ≤ σ ≤1.0); (d-f) time-resolved between-module participation (BT) was maximally variable with increasing σ and across the critical boundary at high γ.
Figure 4 with 2 supplements
Regional clustering results.
(a) regions from the CoCoMac data organized according to rich club (red), feeder (blue) or local (green) status, along with a force-directed plot of the top 10% of connections (aligned by hemisphere), colored according to structural hub connectivity status; (b) the rich club cluster demonstrated an increase in realized mean gain (the relative output as a function of its’ unique topology) at the bifurcation boundary, compared to feeder and local nodes, which showed higher realized gain at high levels of σ and γ; (c) the three clusters of regions also demonstrated differential responses to neural gain; and (d) excitability. The black lines in (c) and (d) denote significant differences in BA between the two groups.
Figure 4—figure supplement 1
Diverse Club.
(a) regional differences in integration (BA) as a function of changes in neural gain; and (b) excitability, separated into regions within or outside the diverse club.
Figure 4—figure supplement 2
Clustering coefficient.
(a) clustering coefficient across the parameter space; (b) as a function of changes in neural gain; and (c) excitability.
Author response image 1
Author response image 2
Additional files
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Transparent reporting form
- https://doi.org/10.7554/eLife.31130.012
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The modulation of neural gain facilitates a transition between functional segregation and integration in the brain
eLife 7:e31130.
https://doi.org/10.7554/eLife.31130
