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A whole-brain model of the neural entropy increase elicited by psychedelic drugs
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A whole-brain model of the neural entropy increase elicited by psychedelic drugs.pdf | Published version | 6.32 MB | Adobe PDF | View/Open |
Title: | A whole-brain model of the neural entropy increase elicited by psychedelic drugs |
Authors: | Herzog, R Mediano, PAM Rosas, FE Lodder, P Carhart-Harris, R Perl, YS Tagliazucchi, E Cofre, R |
Item Type: | Journal Article |
Abstract: | Psychedelic drugs, including lysergic acid diethylamide (LSD) and other agonists of the serotonin 2A receptor (5HT2A-R), induce drastic changes in subjective experience, and provide a unique opportunity to study the neurobiological basis of consciousness. One of the most notable neurophysiological signatures of psychedelics, increased entropy in spontaneous neural activity, is thought to be of relevance to the psychedelic experience, mediating both acute alterations in consciousness and long-term effects. However, no clear mechanistic explanation for this entropy increase has been put forward so far. We sought to do this here by building upon a recent whole-brain model of serotonergic neuromodulation, to study the entropic effects of 5HT2A-R activation. Our results reproduce the overall entropy increase observed in previous experiments in vivo, providing the first model-based explanation for this phenomenon. We also found that entropy changes were not uniform across the brain: entropy increased in all regions, but the larger effect were localised in visuo-occipital regions. Interestingly, at the whole-brain level, this reconfiguration was not well explained by 5HT2A-R density, but related closely to the topological properties of the brain’s anatomical connectivity. These results help us understand the mechanisms underlying the psychedelic state and, more generally, the pharmacological modulation of whole-brain activity. |
Issue Date: | 17-Apr-2023 |
Date of Acceptance: | 30-Mar-2023 |
URI: | http://hdl.handle.net/10044/1/115654 |
DOI: | 10.1038/s41598-023-32649-7 |
ISSN: | 2045-2322 |
Publisher: | Nature Portfolio |
Journal / Book Title: | Scientific Reports |
Volume: | 13 |
Copyright Statement: | © The Author(s) 2023. 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/. |
Publication Status: | Published |
Article Number: | 6244 |
Online Publication Date: | 2023-04-17 |
Appears in Collections: | Computing Faculty of Medicine Department of Brain Sciences Faculty of Engineering |
This item is licensed under a Creative Commons License