On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments
Journal of Physics: Complexity January 22, 2024 Caroline L Alves, Manuel Ciba, Thaise G L de O. Toutain et al. 5 citations
Machine learning applied to EEG data reveals that the psychedelic DMT disrupts the balance between functional segregation and integration in cortical brain networks, making brain activity more entropic. Complex network measures such as closeness centrality best capture these changes, achieving 89% AUC in classifying brain states before and after DMT inhalation. Key connectivity differences involve the temporal and central cortex and the precentral gyrus and lateral occipital cortex, the latter presumably related to emotional, visual, and mystical experiences. Larger communities and longer average path lengths occur under DMT, supporting the view that psychedelics increase brain entropy.