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Exploring electroencephalography with a model inspired by quantum mechanics
An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same n...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8492705/ https://www.ncbi.nlm.nih.gov/pubmed/34611185 http://dx.doi.org/10.1038/s41598-021-97960-7 |
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author | Popiel, Nicholas J. M. Metrow, Colin Laforge, Geoffrey Owen, Adrian M. Stojanoski, Bobby Soddu, Andrea |
author_facet | Popiel, Nicholas J. M. Metrow, Colin Laforge, Geoffrey Owen, Adrian M. Stojanoski, Bobby Soddu, Andrea |
author_sort | Popiel, Nicholas J. M. |
collection | PubMed |
description | An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant K(Brain), which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object. |
format | Online Article Text |
id | pubmed-8492705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84927052021-10-07 Exploring electroencephalography with a model inspired by quantum mechanics Popiel, Nicholas J. M. Metrow, Colin Laforge, Geoffrey Owen, Adrian M. Stojanoski, Bobby Soddu, Andrea Sci Rep Article An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant K(Brain), which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object. Nature Publishing Group UK 2021-10-05 /pmc/articles/PMC8492705/ /pubmed/34611185 http://dx.doi.org/10.1038/s41598-021-97960-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Popiel, Nicholas J. M. Metrow, Colin Laforge, Geoffrey Owen, Adrian M. Stojanoski, Bobby Soddu, Andrea Exploring electroencephalography with a model inspired by quantum mechanics |
title | Exploring electroencephalography with a model inspired by quantum mechanics |
title_full | Exploring electroencephalography with a model inspired by quantum mechanics |
title_fullStr | Exploring electroencephalography with a model inspired by quantum mechanics |
title_full_unstemmed | Exploring electroencephalography with a model inspired by quantum mechanics |
title_short | Exploring electroencephalography with a model inspired by quantum mechanics |
title_sort | exploring electroencephalography with a model inspired by quantum mechanics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8492705/ https://www.ncbi.nlm.nih.gov/pubmed/34611185 http://dx.doi.org/10.1038/s41598-021-97960-7 |
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