@article{AlvesCuryRosteretal.2022, author = {Alves, Caroline L. and Cury, Rubens G. and Roster, Kirstin and Pineda, Aruane M. and Rodrigues, Francisco A. and Thielemann, Christiane and Ciba, Manuel}, title = {Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments}, series = {PLOS ONE}, volume = {2022}, journal = {PLOS ONE}, number = {12}, doi = {https://doi.org/10.1371/journal. pone.0277257}, pages = {1 -- 26}, year = {2022}, abstract = {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}, subject = {Ayahuasca}, language = {en} } @article{AlvesPinedaRosteretal.2022, author = {Alves, Caroline L. and Pineda, Aruane M. and Roster, Kirstin and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia}, series = {Journal of Physics: complexity}, volume = {2022}, journal = {Journal of Physics: complexity}, number = {3}, doi = {DOI 10.1088/2632-072X/ac5f8d}, pages = {1 -- 13}, year = {2022}, abstract = {Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that our approach can classify patients with Alzheimer's disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases, that use raw EEG time series, shows that our method provides the highest precision. Therefore, the application of deep neural networks on data from brain connections is a very promising method for the diagnosis of neurological disorders.}, subject = {Hirnfunktionsst{\"o}rung}, language = {en} }