@unpublished{AlvesPaulodeFariaetal.2025, author = {Alves, Caroline and Paulo, Artur Jos{\´e} Marques and de Faria, Danilo Donizete and Sato, Jo{\~a}o Ricardo and Borges, Vanderci and Silva, Sonia de Azevedo and Ferraz, Henrique Ballalai and Rodrigues, Francisco A. and Thielemann, Christiane and Moeckel, Michael and Aguiar, Patricia de Carvalho}, title = {Decoding Dystonia: unveiling neural patterns with interpretable EEG-Based Machine Learning}, publisher = {Springer Science and Business Media LLC}, doi = {https://doi.org/10.21203/rs.3.rs-7483388/v1}, year = {2025}, abstract = {Dystonia has a multifaceted and complex pathogenesis. Current diagnostic proce-dures, which focus primarily on clinical signs, may lack accuracy due to the variable presentationsof different dystonia types. There is a need for objective, interpretable, and non-invasive diagnostictools. This study aims to develop an interpretable electroencephalography (EEG)-basedmachine learning (ML) and deep learning (DL) approach to distinguish between focal upper limbdystonia (ULD), cervical dystonia (CD), and healthy controls (HC). EEG data were recorded during resting-state, writing-from-memory, and finger-tapping tasks. The EEG signals were segmented into windows to generate connectivity matricesusing various pairwise correlation metrics. Machine learning models were trained to classify thegroups, with performance evaluated using accuracy and area under the curve (AUC) metrics. Our approach achieved accuracy and AUC scores close to 100\%. Transfer entropyemerged as the most effective connectivity metric, revealing altered brain connections in dystonia.Complex network measures outperformed traditional EEG features, highlighting the relevance offunctional connectivity. Resting-state EEG showed the highest classification performance for ULD,suggesting strong diagnostic potential. Conclusions: This study provides the first machine learning-based comparison between differenttypes of dystonia, introduces novel cervical dystonia EEG data, and yields medically interpretableinsights into altered brain connectivity. The findings enhance our understanding of dystonia and support using EEG as alow-cost, interpretable tool for diagnosing and developing brain-machine interfaces.}, subject = {Dystonie}, language = {en} } @article{SallumAlvesdeOToutainetal.2025, author = {Sallum, Loriz Francisco and Alves, Caroline L. and de O Toutain, Thaise Graziele L and Porto, Joel Augusto Moura and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {Revealing patterns in major depressive disorder with machine learning and networks}, series = {Chaos, Solitons \& Fractals}, volume = {194}, journal = {Chaos, Solitons \& Fractals}, publisher = {Elsevier BV}, issn = {0960-0779}, doi = {https://doi.org/10.1016/j.chaos.2025.116163}, year = {2025}, abstract = {Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.}, subject = {Depression}, language = {en} } @article{SallumAlvesThielemannetal.2024, author = {Sallum, Loriz Francisco and Alves, Caroline L. and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {Revealing patterns in major depressive disorder with machine learning and networks}, series = {medrxiv}, volume = {2024}, journal = {medrxiv}, number = {1}, doi = {doi.org/10.1101/2024.06.07.24308619}, pages = {1 -- 17}, year = {2024}, abstract = {Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.}, subject = {Depression}, language = {en} } @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{AlvesWisselCapetianetal.2022, author = {Alves, Caroline L. and Wissel, Lennart and Capetian, Philipp and Thielemann, Christiane}, title = {Functional connectivity and convolutional neural networks for automatic classification of EEG data}, series = {Clinical Neurophysiology}, volume = {2022}, journal = {Clinical Neurophysiology}, number = {137}, doi = {https://doi.org/10.1016/j.clinph.2022.01.086}, pages = {e37 -- e47}, year = {2022}, subject = {Elektroencephalographie}, language = {de} } @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} }