Application of fractals and complex networks to the analysis of EEG signals during DMT experiments

• Grant number: 24/13566-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: October 01, 2024
• End date: September 30, 2025
• Field of knowledge: Physical Sciences and Mathematics - Physics
• Principal Investigator: Gabriela Castellano
• Grantee: Eduardo Abreu da Silva
• Host Institution: Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology, AP.CEPID

Abstract

N,N-Dimethyltryptamine (DMT) is a natural serotonergic psychedelic that has attracted great scientific interest due to its profound psychoactive effects. In recent years, there has been a resurgence in therapeutic interest in psychedelics, which have shown promise in treating conditions such as depression, anxiety and post-traumatic stress disorder. Some studies have used electroencephalography (EEG) signals to better understand the cerebral effects of DMT, particularly its effect on the aforementioned conditions. EEG signal analysis methods such as fractal decomposition and functional connectivity have been applied to data from people under the effect of DMT, but there are still discrepancies in the literature. Yet, these methods have been applied independently. Therefore, the main objective of this project is the joint use of fractals and functional connectivity, applied to EEG signals from individuals before and after inhaling DMT, to better understand the cerebral effects of this substance. Machine learning algorithms will also be used to help distinguish between individuals' different brain states, and explanatory machine learning methods will be used to determine which characteristics are actually most prominent in differentiating between states.