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Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain.
Li, Mike; Han, Yinuo; Aburn, Matthew J; Breakspear, Michael; Poldrack, Russell A; Shine, James M; Lizier, Joseph T.
Afiliação
  • Li M; Centre for Complex Systems, The University of Sydney, Sydney, Australia.
  • Han Y; Brain and Mind Centre, The University of Sydney, Sydney, Australia.
  • Aburn MJ; Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Sydney, Australia.
  • Breakspear M; Centre for Complex Systems, The University of Sydney, Sydney, Australia.
  • Poldrack RA; Brain and Mind Centre, The University of Sydney, Sydney, Australia.
  • Shine JM; QIMR Berghofer Medical Research Institute, Queensland, Australia.
  • Lizier JT; QIMR Berghofer Medical Research Institute, Queensland, Australia.
PLoS Comput Biol ; 15(10): e1006957, 2019 10.
Article em En | MEDLINE | ID: mdl-31613882
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico / Processos Mentais Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico / Processos Mentais Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article