TY - JOUR A1 - Alves, Caroline L. A1 - Pineda, Aruane M. A1 - Roster, Kirstin A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia JF - Journal of Physics: complexity N2 - 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. KW - complex networks KW - Machine learning KW - Hirnfunktionsstörung KW - Alzheimerkrankheit KW - Schizophrenie KW - Elektroencephalographie Y1 - 2022 U6 - https://doi.org/DOI 10.1088/2632-072X/ac5f8d VL - 2022 IS - 3 SP - 1 EP - 13 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Toutain, Thaise A1 - Porto, Joel A1 - Aguiar, Patricia A1 - de Sena, Eduardo Pondé A1 - Rodrigues, Francisco A. A1 - Pineda, Aruane M. A1 - Thielemann, Christiane T1 - Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia JF - Journal of Neural Engineering N2 - Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain. KW - Maschinelles Lernen KW - Schizophrenie KW - Deep Learning Y1 - 2023 U6 - https://doi.org/10.1088/1741-2552/acf734 VL - 2023 IS - 20/5 SP - 0 EP - 0 ER -