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Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

  • 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

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Author:Caroline Alves, Rubens G. Cury, Kirstin Roster, Aruane M. Pineda, Francisco A. Rodrigues, Christiane Thielemann, Manuel Ciba
DOI:https://doi.org/https://doi.org/10.1371/journal. pone.0277257
Parent Title (English):PLOS ONE
Document Type:Article
Language:English
Year of Completion:2022
Date of first Publication:2022/12/16
Release Date:2023/01/10
GND Keyword:Ayahuasca; Elektroencephalographie; Alzheimerkrankheit; Gehirn
Volume:2022
Issue:12
First Page:1
Last Page:26
Urheberrecht:1
Institutes:Fakultät Ingenieurwissenschaften / Labore / BIOMEMS Lab
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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