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.
A machine-learning method using support vector machines classified EEG data from volunteers before and after inhaling the psychedelic DMT. Complex network measures derived from brain connectivity achieved 89% AUC, outperforming raw connectivity matrices. Key distinguishing features included connections between temporal and central cortex regions (TP8-C3) linked to finger movements, and between precentral gyrus and lateral occipital cortex (FC5-P8) potentially related to emotional and mystical experiences. Closeness centrality was the most important network measure. DMT increased community size and average path length, disrupting the balance between functional segregation and integration, supporting the idea that cortical activity becomes more entropic under psychedelics.