Information parity increases on functional brain networks under influence of a psychedelic substance

• Autor(a) principal: Viol, Aline
• Data de Publicação: 2023
• Outros Autores: Mohan, Madras Viswanathan Gandhi, Soldatkina, Oleksandra, Fontes, Fernanda Palhano Xavier de, Ventura, Heloisa Helena dos Santos Onias, Araujo, Draulio Barros de, Hövel, Philipp
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/handle/123456789/52228
• topic: Complex networks; Information parity; Psychedelics; Magnetic Resonance Imaging; fMRI; Ayahuasca; Human brain networks; Neural correlates

The physical basis of consciousness is one of the most intriguing open questions that contemporary science aims to solve. By approaching the brain as an interactive information system, complex network theory has greatly contributed to understand brain process in different states of mind. We study a non-ordinary state of mind by comparing resting-state functional brain networks of individuals in two different conditions: before and after the ingestion of the psychedelic brew Ayahuasca. In order to quantify the functional, statistical symmetries between brain region connectivity, we calculate the pairwise information parity of the functional brain networks. Unlike the usual approach to quantitative network analysis that considers only local or global scales, information parity instead quantifies pairwise statistical similarities over the entire network structure. We find an increase in the average information parity on brain networks of individuals under psychedelic influences. Notably, the information parity between regions from the limbic system and frontal cortex is consistently higher for all the individuals while under the psychedelic influence. These findings suggest that the resemblance of statistical influences between pair of brain regions activities tends to increase under Ayahuasca effects. This could be interpreted as a mechanism to maintain the network functional resilience