ArticlePLoS computational biology2019
Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Trial
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- Emergent flow theory: autonomic coherence as a gain parameter for the temporal architecture of conscious integration.Frontiers in integrative neuroscience · 2026Article
- Toward a unified taxonomy of information dynamics via Integrated Information Decomposition.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Revealing non-trivial information structures in aneural biological tissues via functional connectivity.PLoS computational biology · 2025Article
- Structured Dynamics in the Algorithmic Agent.Entropy (Basel, Switzerland) · 2025Article
- Structural inequality and temporal brain dynamics across diverse samples.Clinical and translational medicine · 2024Article
- The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro.Micromachines · 2024Article
- The Constrained Disorder Principle May Account for Consciousness.Brain sciences · 2024Review
- Fractal basins as a mechanism for the nimble brain.Scientific reports · 2023Article
- Neuromodulatory control of complex adaptive dynamics in the brain.Interface focus · 2023Review
- Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Masked features of task states found in individual brain networks.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle.Network neuroscience (Cambridge, Mass.) · 2023Article
- Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Review
- Astrocytes Learn to Detect and Signal Deviations From Critical Brain Dynamics.Neural computation · 2022Article
- May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data.Journal of the Royal Society, Interface · 2022Review
- Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Early lock-in of structured and specialised information flows during neural development.eLife · 2022Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
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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