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A synergistic workspace for human consciousness revealed by integrated information decomposition

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Title: A synergistic workspace for human consciousness revealed by integrated information decomposition
Authors: Luppi, AI
Mediano, PAM
Rosas, FE
Allanson, J
Pickard, J
Carhart-Harris, RL
Williams, GB
Craig, MM
Finoia, P
Owen, AM
Naci, L
Menon, DK
Bor, D
Stamatakis, EA
Item Type: Journal Article
Abstract: How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a ‘synergistic global workspace’, comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain’s default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
Issue Date: Jul-2024
Date of Acceptance: 1-Jul-2024
URI: http://hdl.handle.net/10044/1/115940
DOI: 10.7554/eLife.88173
ISSN: 2050-084X
Publisher: eLife Sciences Publications Ltd
Journal / Book Title: eLife
Volume: 12
Copyright Statement: © 2023, Luppi et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Publication Status: Published
Article Number: RP88173
Online Publication Date: 2024-07-18
Appears in Collections:Computing
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Faculty of Medicine
Department of Brain Sciences
Faculty of Engineering



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