Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on emb...

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Main Authors: Satohiro Tajima, Toru Yanagawa, Naotaka Fujii, Taro Toyoizumi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-11-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1004537
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spelling doaj-e5d082addff04dfb8a2d3944dfc9170e2021-04-21T14:59:08ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-11-011111e100453710.1371/journal.pcbi.1004537Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.Satohiro TajimaToru YanagawaNaotaka FujiiTaro ToyoizumiBrain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.https://doi.org/10.1371/journal.pcbi.1004537
collection DOAJ
language English
format Article
sources DOAJ
author Satohiro Tajima
Toru Yanagawa
Naotaka Fujii
Taro Toyoizumi
spellingShingle Satohiro Tajima
Toru Yanagawa
Naotaka Fujii
Taro Toyoizumi
Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.
PLoS Computational Biology
author_facet Satohiro Tajima
Toru Yanagawa
Naotaka Fujii
Taro Toyoizumi
author_sort Satohiro Tajima
title Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.
title_short Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.
title_full Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.
title_fullStr Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.
title_full_unstemmed Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.
title_sort untangling brain-wide dynamics in consciousness by cross-embedding.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2015-11-01
description Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.
url https://doi.org/10.1371/journal.pcbi.1004537
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