@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{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{AlvesRodriguesSallumetal.2024, author = {Alves, Caroline L. and Rodrigues, Francisco A. and Sallum, Loriz Francisco and Toutain, Thaise and Porto, Joel and Aguiar, Patricia and Thielemann, Christiane and M{\"o}ckel, Michael}, title = {Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis}, series = {plos one}, volume = {2024}, journal = {plos one}, number = {19(10)}, doi = {https://doi.org/10.1371/journal.pone.0305630}, pages = {1 -- 35}, year = {2024}, abstract = {Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98\%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML c}, subject = {Aufmerksamkeitsdefizit-Syndrom}, language = {en} }