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 demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific br...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0277257 |
| _version_ | 1797956144696655872 |
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| author | Caroline L Alves Rubens Gisbert Cury Kirstin Roster Aruane M Pineda Francisco A Rodrigues Christiane Thielemann Manuel Ciba |
| author_facet | Caroline L Alves Rubens Gisbert Cury Kirstin Roster Aruane M Pineda Francisco A Rodrigues Christiane Thielemann Manuel Ciba |
| author_sort | Caroline L Alves |
| collection | DOAJ |
| description | 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 demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments 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 important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity 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 suggested 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. |
| first_indexed | 2024-04-10T23:44:18Z |
| format | Article |
| id | doaj.art-fdd75bc6805f4024a01b1b73f02f2736 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T23:44:18Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-fdd75bc6805f4024a01b1b73f02f27362023-01-11T05:32:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712e027725710.1371/journal.pone.0277257Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments.Caroline L AlvesRubens Gisbert CuryKirstin RosterAruane M PinedaFrancisco A RodriguesChristiane ThielemannManuel CibaAyahuasca 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 demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments 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 important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity 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 suggested 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.https://doi.org/10.1371/journal.pone.0277257 |
| spellingShingle | Caroline L Alves Rubens Gisbert Cury Kirstin Roster Aruane M Pineda Francisco A Rodrigues Christiane Thielemann Manuel Ciba Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. PLoS ONE |
| title | Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. |
| title_full | Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. |
| title_fullStr | Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. |
| title_full_unstemmed | Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. |
| title_short | Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. |
| title_sort | application of machine learning and complex network measures to an eeg dataset from ayahuasca experiments |
| url | https://doi.org/10.1371/journal.pone.0277257 |
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