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. 2019 Oct 15;15(10):e1006957.
doi: 10.1371/journal.pcbi.1006957. eCollection 2019 Oct.

Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain

Mike Li  1   2   3 , Yinuo Han  1   2 , Matthew J Aburn  4 , Michael Breakspear  4 , Russell A Poldrack  5 , James M Shine  1   2 , Joseph T Lizier  1   3
Affiliations

Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain

Mike Li et al. PLoS Comput Biol. .

Abstract

A key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system, via changes in neural gain (in terms of the amplification and non-linearity in stimulus-response transfer function of brain regions). In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain parameters led to a 'critical' transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain parameters would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schematic diagram showing how neural gain parameters (e.g. under modulation by noradrenaline) may potentially affect the information processing structure of the brain.
(a) The effect of neural gain (σ) and excitability (γ), the two tuning parameters being varied in our neural mass model (see Methods), on the response of individual neurons to stimuli are shown schematically. Each input stimulus to a target region in the model contributes an effect to the rate of change of the target via a sigmoid function. Arrows in the figures indicate how the sigmoid function defining these effects changes with increases in these gain parameters (with σ increasing nonlinearity of response and γ increase amplification). (b) Previous results from [7] (adapted under Creative Commons Attribution License CC BY 4.0) showing that varying neural gain and excitability may cause abrupt changes in the mean phase synchrony of the brain from modelled fMRI BOLD recordings, implying the existence of a critical boundary between a segregated phase (“S”, low phase synchrony) and an integrated phase (“I”, high phase synchrony) in the brain. (c) Schematic diagram of functional brain networks in the segregated and integrated phases, and how changing neural gain and excitability may lead to transitions between the two. (d) Schematic diagram of the concept of active information storage and transfer entropy, and how they may be affected by phase transitions. Qualitatively, active information storage (green arrow) describes information on the next instance Xn+1 (blue sample) of a time series X provided by its own history (Xn(k), green samples), whereas transfer entropy (orange arrow) describes that provided by the past (Yn(l), orange samples) of another time series Y in the context of the target’s history. See further details on these measures in Methods.
Fig 2
Fig 2. Measures of information storage.
(a) Active memory utilization rate. (b) and (c) Mean active memory rate across σ and γ phase boundaries. (d) Network motifs supporting information storage in the dynamics of node a. (e) Correlation of AM rate to local network support (weighted motif counts). (f) Correlation of AM rate to normalized within-region synaptic connection weight. Matching colour scale is used for (e) and (f). By convention we use blue-white-red color scale for correlation plots to emphasise the positive-negative distinction, and default blue-yellow scale for other plots.
Fig 3
Fig 3. Measures of information transfer.
(a) Average transfer entropy rate over causal edges (those connected source → target by the directed connectome). (b) Conditional transfer entropy rate over causal edges. (c) Collective transfer entropy rate of causal edges. (d) and (e) Mean TE rate across σ boundary and γ boundaries.
Fig 4
Fig 4. Correlations between information transfer and node degrees.
(a) Correlation between TE rate and source in-degree. (b) Correlation between conditional TE rate and source in-degree. (c) Correlation between collective TE rate and target in-degree. Matching scale is used across all subfigures.
Fig 5
Fig 5. Information transfer between hemispheres.
(a) TE rate over interhemisphere causal edges. (b) Proportion of significant TE rate measurements occuring between interhemisphere source and target.
Fig 6
Fig 6. Phase portrait showing six identified regions.
A transparent figure of the TE rate from Fig 3a is shown behind for comparison. Dotted lines represent a looser boundary, which are not observed in all measures.
See this image and copyright information in PMC

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Grants and funding

MJA was supported through a Queensland Government Advance Queensland Innovation Partnership grant AQIP12316-17RD2 - https://advance.qld.gov.au/investors-universities-and-researchers/innovation-partnerships. JL was supported through the Australian Research Council DECRA grant DE160100630 - https://www.arc.gov.au/grants/discovery-program/discovery-early-career-researcher-award-decra. JMS was supported through a University of Sydney Robinson Fellowship and NHMRC Project Grant 1156536 - https://nhmrc.gov.au/funding/find-funding/project-grants. JMS and JL were supported through The University of Sydney Research Accelerator (SOAR) Fellowship program - https://sydney.edu.au/research/our-researchers/sydney-research-accelerator-fellows.html. High performance computing facilities provided by QIMR Berghofer Medical Research Institute and The University of Sydney (artemis) have contributed to the research results reported within this paper. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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