Entropia de Shannon e propriedades topológicas de redes funcionais do cérebro humano sob efeito de Ayahuasca

• Ano de defesa: 2015
• Autor(a) principal: Barbosa, Aline Amabile Viol
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM FÍSICA
• Palavras-chave em Português: Cérebro humano; Redes funcionais; Entropia de Shannon; Ayahuasca; DMT; Estado alterado de consciência; Neurociência; Sistemas complexos; CNPQ::CIENCIAS EXATAS E DA TERRA::FISICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/21549

An important problem in nonlinear dynamics and statistical physics relates to the quantitative description of the behavior of complex systems. The human brain is one such system. Methods and concepts used in physics have contributed to the development of diverse fields, including neuroscience. In this thesis, we investigate the behavior of the human brain in altered states of consciousness. We study the functional maps of the brain generated by functional magnetic resonance imaging (fMRI), using the tools of statistical physics and the theory of complex networks. We analyze resting state fMRI data of the brains of 9 human subjects under two distinct conditions: under normal waking state and in an altered state of consciousness, induced by ingestion of the psychoactive infusion known as Ayahuasca, of Amazonian indigenous origin. Our study was broadly motivated by two questions: Does Ayahuasca affect the functional brain networks? How can we quantify these effects? We initially constructed complex network models of the brain using the fMRI data, before and after ingestion of Ayahuasca. We next analyzed the statistical and topological properties of these networks. Comparing the networks generated from the data before and after Ayahuasca ingestion, we find some significant changes which we highlight: an increase in the Shannon entropy, a increase in the mean geodesic distance and changes in network efficiencies. The increase in mean distance indicates a global expansion of the brain networks. This suggests a decrease in global integration of brain regions. Moreover, the increase in the entropy of the degree distribution suggests an increase in the range of possibilities of functional patterns. The change in the network efficiencies goes beyond what can be accounted for by the changes in degree distribution. We discuss and present potential interpretations of our results in the context of neuroscience.