- Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by
the inhabitants of this region for hundreds of years. Furthermore, this plant has been demon� strated to be a viable therapy for a variety of neurological and mental diseases. EEG experi� ments have found specific brain regions that changed significantly due to ayahuasca. Here,
we used an EEG dataset to investigate the ability to automatically detect changes in brain
activity using machine learning and complex networks. Machine learning was applied at
three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of
the EEG time series, and (C) the complex network measures calculated from (B). Further, at
the abstraction level of (C), we developed new measures of complex networks relating to
community detection. As a result, the machine learning method was able to automatically
detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed
by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are
more important for the detection of ayahuasca. The most activated areas were the frontal
and temporal lobe, which is consistent with the literature. F3 and PO4 were the most impor� tant brain connections, a significant new discovery for psychedelic literature. This connec� tion may point to a cognitive process akin to face recognition in individuals during
ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assorta� tivity were the most important complex network measures. These two measures are also
associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic
mechanism. Moreover, the new measures were crucial to the predictive model and sug� gested larger brain communities associated with the use of ayahuasca. This suggests that
the dissemination of information in functional brain networks is slower when this drug is
present. Overall, our methodology was able to automatically detect changes in brain activity
during ayahuasca consumption and interpret how these psychedelics alter brain networks,
as well as provide insights into their mechanisms of action